We thank Mr. Shijiang Shen for his advice on apparatus design and Ms. Guihua Wang for her assistance with animal care and training. This work was supported by National Key Research and Development Program of China (2017YFC1308501), the National Natural Science Foundation of China (31627802), the Public Projects of Zhejiang Province (2016C33059), and the Fundamental Research Funds for the Central Universities.

All behavioral and surgical procedures conformed to the Guide for the Care and Use of Laboratory Animals (China Ministry of Health) and were approved by the Animal Care Committee at Zhejiang University, China.
1.Assembling the 3D translational device
<ol>
	<li>Build a frame of size 920 mm x 690 mm x 530 mm with aluminum construction rails (cross section: 40 mm x 40 mm).</li>
	<li>Secure four pedestals to the two ends of the Y-rails with screws (M4) (Figure 1B).</li>
	<li>Fix two Y-rails onto the top surface of the frame in parallel by securing the four pedestals to the four corners of the top surface with screws (M6) (Figure 1B).</li>
	<li>Connect two Y-rails with a connecting shaft and two diaphragm couplings. Tighten the lock screws of couplings to synchronize the shafts of two rails (Figure 1B).</li>
	<li>Put six nuts (M4) into the back grooves of the Z-rail. Attach one side of the right triangle frame to the back of the Z-rail with screws.</li>
	<li>Pull the triangle frame to the end that is distal to the shaft and tighten the screws. Attach the other right triangle frame to the other Z-rail in the same way (Figure 1C).</li>
	<li>Secure the other right-angled sides of two triangle frames to the sliders of two Y-rails with screws (M6) (Figure 1C).</li>
	<li>Connect two Z-rails with a connecting shaft and diaphragm couplings and tighten the lock screws of coupling (Figure 1C).</li>
	<li>Attach the two T-shaped connecting boards to the back of the X-rail with nuts and screws (M4). Then pull the two T-shaped boards to the two ends of X-rail and tighten the screws (Figure 1D).</li>
	<li>Secure the two T-shaped connecting boards onto the sliders of two Z-rails with screws (M6), respectively (Figure 1D).</li>
	<li>Insert the stepping motor into the shaft hole of the gear reducer and screw their flanges together&#160;(Figure 1E).</li>
	<li>Secure the connecting ring to the shaft end of the active X-rail with screws (M4).</li>
	<li>Insert the shaft of X-rail into the coupling and fix the gear reducer to the connecting ring with screws (M4).&#160; Tighten the lock screws of the coupling (Figure 1E).</li>
	<li>Fix the other two stepping motors and gear reducers to the active Y-rail and Z-rail using the methods described in steps 1.11&#8211;1.12.</li>
	<li>Insert the power and control cables of the three stepping&#160;motors to the power and control ports of their&#160;drivers, respectively and secure the cables with screws on the driver side.</li>
</ol>
2. Assembling the turning table
<ol>
	<li>Download the .DWG design files from the Supplemental Files of this paper. Prepare the objects, mental shaft, locating bar, rotator and case by 3D printing or mechanical processing.</li>
	<li>Put the touch sensors into the groove of the object body and stick them onto the predefined touch areas with double sided tape (Figure 2B). 
	NOTE: Each object consists of four subcomponents: a backboard, object body with groove inside, cover board, and touch sensors.</li>
	<li>Pass the wires through the hole of the object backboard and secure the cover board onto the object body with screws (Figure 2B).</li>
	<li>Pass wires of touch sensors through the holes on the sides of rotator&#160;and fix the objects onto the rotator with screws.&#160;(Figure 2C).</li>
	<li>Solder the wire ends of touch sensors to the rotating wire ends of the electric slip ring and wrap the joints with electrical tape (Figure 2D).</li>
	<li>Secure the case to the slider of the X-rail with screws. Place the bearing in the bottom hole of the box and secure the locating bar to the top surface of case with screws (Figure 2E).</li>
	<li>Place the rotator into the case from side, coinciding the axes of rotator, bearing and box. Pass the wires of the electric slip ring through the top hole of the case (Figure 2F).</li>
	<li>Insert the metal shaft into the bearing from the top hole of case and fit the shaft key to the keyway of the rotator (Figure 2G).</li>
	<li>Set the electric slip ring around the metal shaft. Place the end of locating bar into the notch of electric slip ring to prevent the outer ring from rotating (Figure 2G).</li>
	<li>Insert the shaft of stepping motor into the hole of metal shaft and secure the motor on the top of the box with screws. (Figure 2H).</li>
	<li>Insert the power and control cables of the motor into the power and control ports of its driver and secure them with screws.</li>
	<li>Stick a tricolor LED (RGB) onto the front side of the case with tape and fix the right side board onto the case.</li>
</ol>
3. Setup of the control system
<ol>
	<li>Insert the direction and pulse control wires of the four motor drivers into the digital I/O ports (pins 81, 83, 85, 87) and digital counter ports (pins 89, 91, 93, 95) of the data acquisition (DAQ) board, respectively. Secure the wires with screws.</li>
	<li>Insert the control wires of LED (green color used for the &#8220;go&#8221; cue, blue color used for the &#8220;error&#8221; cue, and red color representing idle) into the digital I/O ports (pin 65 and 66) of the DAQ card and secure them with screws.</li>
	<li>Insert the output wires of touch sensors and switch button into the digital I/O ports (pin 67&#8211;77) of the DAQ board and secure the wires with screws.</li>
	<li>Insert the start-stop and direction control wires of the peristaltic pump into the digital I/O pins 1 and 80, respectively. Insert the flow velocity control wire into the analog I/O port AO2. Secure the wires with screws.</li>
	<li>Setup a motion capture system as described by the manufacturer to record the hand trajectory in 3D space. 
	NOTE: A commercial motion capture system (see Table of Materials) was used, which consists of eight cameras, a power hub, an Ethernet switch and a supporting software (e.g., Cortex). Please refer to the manual to get more details about setup of the system.</li>
	<li>Setup a neural signal acquisition system as described by the manufacturer to record electrophysiology signal from subject. 
	NOTE: A commercial data acquisition system (Table of Materials) was used, which consists of a neural signal processor (NSP), front-end amplifier (FEA), amplifier power supply (ASP), head stages, and its supporting software (e.g., Central). Refer to the manual for more details about the setup of the system.</li>
</ol>
4. Preparation of the experimental session
<ol>
	<li>Initialize the 3D translational device and the turning table. Specifically, pull the sliders of all linear slide rail to the starting point (lower left corner) and turn the first object (i.e., the vertically placed handle) of turning table to face the front side of the turning table.</li>
	<li>Power on the experimental devices, including motion capture system, neural signal acquisition, DAQ board, peristaltic pump, and four motors.</li>
	<li>Setup the paradigm software (Figure 3A).
	<ol>
		<li>Double click Paradigm.exe to open the paradigm software (available on request).</li>
		<li>Define the number of the reaching positions and their 3D coordinates (x, y, and z, in millimeters) relative to initial positions (step 4.2).</li>
		<li>Write coordinates of all positions in the form of matrix in a .txt document. Make sure that each row includes the x-, y-, and z-coordinates of one position separated with a space. Save the txt document.</li>
		<li>Click Open File in the Pool panel of paradigm software and select the .txt document saved before to load the presentation positions into the paradigm software. 
		NOTE: In this study, eight target positions were set according to animal&#8217;s reaching range, which are located at vertices of a cuboid workspace9,10 (90 mm x 60 mm x 90 mm).</li>
		<li>Check the objects to be presented in the experiment in the Object Pool of paradigm software.</li>
		<li>Adjust experimental parameters in the Time Parameters panel of paradigm software. Set Baseline = 400 ms, Motor Run = 2,000 ms, Planning = 1,000 ms, max Reaction Time = 500 ms, max Reach Time = 1,000 ms, min Hold Time = 500 ms, Reward = 60 ms, and Error Cue = 1,000 ms.</li>
	</ol>
	</li>
	<li>Seat the rhesus monkey (with a micro-electrode array implanted in motor cortex) on the monkey chair by inserting its collar into the groove of chair and fixing its head.</li>
	<li>Fix the monkey chair to the aluminum construction frame. Keep the head 250 mm away from the front side of the cuboid and keep the eyes 50 mm above the top side of the cuboid workspace (horizontal visual angle: 20&#176;; vertical visual angle: 18&#176;).</li>
	<li>Construct a tracking template of motion capture system.
	<ol>
		<li>Attach three reflective markers at the end of the arm (close to wrist) with double-sided tape. Make sure that the three markers form a scalene triangle.</li>
		<li>Click the Run button of the paradigm software to start the task.</li>
		<li>Click the Record button on the Motion Capture panel of Cortex software to record trajectories of three markers for 60 s when the monkey is doing the task. Click the Stop button to suspend the experiment.</li>
		<li>Build a tracking template of three markers on the Cortex software using the recorded trajectories and save the template. 
		NOTE: Please refer to the manual of Cortex to get more details about how to build a model.</li>
	</ol>
	</li>
	<li>Connect the GND ports of FEA and micro-electrode array implanted in the monkey&#8217;s motor cortex with a wire and pinch cocks. Then insert the head stages into the connector of the micro-electrode array31.</li>
	<li>Open the Central software of neural signal acquisition system and set recording parameters including storage path, line noise cancellation, spike filter, spike threshold, etc. 
	NOTE: Please refer to the manual of neural signal acquisition system for more details of software setting.</li>
	<li>Open the synchronization software (Figure 3B, available on request). Click the three Connect buttons in the Cerebus, Motion Capture, and Paradigm panels to connect the synchronization software with the neural signal acquisition system, motion capture system and paradigm software, respectively.</li>
	<li>Click the Run button of the paradigm software to continue the experiment.</li>
	<li>Click the Record button on the File Storage panel of Central software to start recording the neural signals.</li>
	<li>Check the saved tracking template and click the Record button on the Motion Capture panel of Cortex software to start recording the trajectory of monkey&#8217;s wrist.</li>
</ol>

<ol>
	<li>Leone, F. T., Monaco, S., Henriques, D. Y., Toni, I., Medendorp, W. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flexible+Reference+Frames+for+Grasp+Planning+in+Human+Parietofrontal+Cortex.">Flexible Reference Frames for Grasp Planning in Human Parietofrontal Cortex.</a> eNeuro. 2 (3), (2015).</li><li>Caminiti, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Early+coding+of+reaching:+frontal+and+parietal+association+connections+of+parieto-occipital+cortex.">Early coding of reaching: frontal and parietal association connections of parieto-occipital cortex.</a> European Journal of Neuroscience. 11 (9), 3339-3345 (1999).</li><li>Georgopoulos, A. P., Schwartz, A. B., Kettner, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuronal+population+coding+of+movement+direction.">Neuronal population coding of movement direction.</a> Science. 233 (4771), 1416-1419 (1986).</li><li>Fu, Q. G., Flament, D., Coltz, J. D., Ebner, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Temporal+encoding+of+movement+kinematics+in+the+discharge+of+primate+primary+motor+and+premotor+neurons.">Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons.</a> Journal of Neurophysiology. 73 (2), 836-854 (1995).</li><li>Moran, D. W., Schwartz, A. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Motor+cortical+representation+of+speed+and+direction+during+reaching.">Motor cortical representation of speed and direction during reaching.</a> Journal of Neurophysiology. 82 (5), 2676-2692 (1999).</li><li>Carmena, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Learning+to+control+a+brain-machine+interface+for+reaching+and+grasping+by+primates.">Learning to control a brain-machine interface for reaching and grasping by primates.</a> PLoS Biology. 1 (2), E42 (2003).</li><li>Li, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prior+Knowledge+of+Target+Direction+and+Intended+Movement+Selection+Improves+Indirect+Reaching+Movement+Decoding.">Prior Knowledge of Target Direction and Intended Movement Selection Improves Indirect Reaching Movement Decoding.</a> Behavioral Neurology. , 2182843 (2017).</li><li>Reina, G. A., Moran, D. W., Schwartz, A. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+the+relationship+between+joint+angular+velocity+and+motor+cortical+discharge+during+reaching.">On the relationship between joint angular velocity and motor cortical discharge during reaching.</a> Journal of Neurophysiology. 85 (6), 2576-2589 (2001).</li><li>Taylor, D. M., Tillery, S. I., Schwartz, A. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Direct+cortical+control+of+3D+neuroprosthetic+devices.">Direct cortical control of 3D neuroprosthetic devices.</a> Science. 296 (5574), 1829-1832 (2002).</li><li>Wang, W., Chan, S. S., Heldman, D. A., Moran, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Motor+cortical+representation+of+hand+translation+and+rotation+during+reaching.">Motor cortical representation of hand translation and rotation during reaching.</a> Journal of Neuroscience. 30 (3), 958-962 (2010).</li><li>Murata, A., Gallese, V., Luppino, G., Kaseda, M., Sakata, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Selectivity+for+the+shape,+size,+and+orientation+of+objects+for+grasping+in+neurons+of+monkey+parietal+area+AIP.">Selectivity for the shape, size, and orientation of objects for grasping in neurons of monkey parietal area AIP.</a> Journal of Neurophysiology. 83 (5), 2580-2601 (2000).</li><li>Raos, V., Umilt&#225;, M. A., Murata, A., Fogassi, L., Gallese, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+Properties+of+Grasping-Related+Neurons+in+the+Ventral+Premotor+Area+F5+of+the+Macaque+Monkey.">Functional Properties of Grasping-Related Neurons in the Ventral Premotor Area F5 of the Macaque Monkey.</a> Journal of Neurophysiology. 95 (2), 709 (2006).</li><li>Schaffelhofer, S., Scherberger, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Object+vision+to+hand+action+in+macaque+parietal,+premotor,+and+motor+cortices.">Object vision to hand action in macaque parietal, premotor, and motor cortices.</a> eLife. 5, (2016).</li><li>Baumann, M. A., Fluet, M. C., Scherberger, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Context-specific+grasp+movement+representation+in+the+macaque+anterior+intraparietal+area.">Context-specific grasp movement representation in the macaque anterior intraparietal area.</a> Journal of Neuroscience. 29 (20), 6436-6448 (2009).</li><li>Riehle, A., Wirtssohn, S., Grun, S., Brochier, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+the+spatio-temporal+structure+of+motor+cortical+LFP+and+spiking+activities+during+reach-to-grasp+movements.">Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements.</a> Frontiers in Neural Circuits. 7, 48 (2013).</li><li>Michaels, J. A., Scherberger, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Population+coding+of+grasp+and+laterality-related+information+in+the+macaque+fronto-parietal+network.">Population coding of grasp and laterality-related information in the macaque fronto-parietal network.</a> Scientific Reports. 8 (1), 1710 (2018).</li><li>Fattori, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hand+orientation+during+reach-to-grasp+movements+modulates+neuronal+activity+in+the+medial+posterior+parietal+area+V6A.">Hand orientation during reach-to-grasp movements modulates neuronal activity in the medial posterior parietal area V6A.</a> Journal of Neuroscience. 29 (6), 1928-1936 (2009).</li><li>Asher, I., Stark, E., Abeles, M., Prut, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+direction+and+object+selectivity+of+local+field+potentials+and+single+units+in+macaque+posterior+parietal+cortex+during+prehension.">Comparison of direction and object selectivity of local field potentials and single units in macaque posterior parietal cortex during prehension.</a> Journal of Neurophysiology. 97 (5), 3684-3695 (2007).</li><li>Stark, E., Asher, I., Abeles, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Encoding+of+reach+and+grasp+by+single+neurons+in+premotor+cortex+is+independent+of+recording+site.">Encoding of reach and grasp by single neurons in premotor cortex is independent of recording site.</a> Journal of Neurophysiology. 97 (5), 3351-3364 (2007).</li><li>Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S., Schwartz, A. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+control+of+a+prosthetic+arm+for+self-feeding.">Cortical control of a prosthetic arm for self-feeding.</a> Nature. 453 (7198), 1098-1101 (2008).</li><li>Vargas-Irwin, C. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Decoding+complete+reach+and+grasp+actions+from+local+primary+motor+cortex+populations.">Decoding complete reach and grasp actions from local primary motor cortex populations.</a> Journal of Neuroscience. 30 (29), 9659-9669 (2010).</li><li>Mollazadeh, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatiotemporal+variation+of+multiple+neurophysiological+signals+in+the+primary+motor+cortex+during+dexterous+reach-to-grasp+movements.">Spatiotemporal variation of multiple neurophysiological signals in the primary motor cortex during dexterous reach-to-grasp movements.</a> Journal of Neuroscience. 31 (43), 15531-15543 (2011).</li><li>Saleh, M., Takahashi, K., Hatsopoulos, N. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Encoding+of+coordinated+reach+and+grasp+trajectories+in+primary+motor+cortex.">Encoding of coordinated reach and grasp trajectories in primary motor cortex.</a> Journal of Neuroscience. 32 (4), 1220-1232 (2012).</li><li>Collinger, J. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-performance+neuroprosthetic+control+by+an+individual+with+tetraplegia.">High-performance neuroprosthetic control by an individual with tetraplegia.</a> The Lancet. 381 (9866), 557-564 (2013).</li><li>Lehmann, S. J., Scherberger, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reach+and+gaze+representations+in+macaque+parietal+and+premotor+grasp+areas.">Reach and gaze representations in macaque parietal and premotor grasp areas.</a> Journal of Neuroscience. 33 (16), 7038-7049 (2013).</li><li>Rouse, A. G., Schieber, M. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatiotemporal+distribution+of+location+and+object+effects+in+reach-to-grasp+kinematics.">Spatiotemporal distribution of location and object effects in reach-to-grasp kinematics.</a> Journal of Neuroscience. 114 (6), 3268-3282 (2015).</li><li>Rouse, A. G., Schieber, M. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatiotemporal+Distribution+of+Location+and+Object+effects+in+Primary+Motor+Cortex+Neurons+during+Reach-to-Grasp.">Spatiotemporal Distribution of Location and Object effects in Primary Motor Cortex Neurons during Reach-to-Grasp.</a> Journal of Neuroscience. 36 (41), 10640-10653 (2016).</li><li>Hao, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+synergies+for+controlling+reach+and+grasp+movement+in+macaques.">Neural synergies for controlling reach and grasp movement in macaques.</a> Neuroscience. 357, 372-383 (2017).</li><li>Takahashi, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Encoding+of+Both+Reaching+and+Grasping+Kinematics+in+Dorsal+and+Ventral+Premotor+Cortices.">Encoding of Both Reaching and Grasping Kinematics in Dorsal and Ventral Premotor Cortices.</a> Journal of Neuroscience. 37 (7), 1733-1746 (2017).</li><li>Chen, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+automated+behavioral+apparatus+to+combine+parameterized+reaching+and+grasping+movements+in+3D+space.">An automated behavioral apparatus to combine parameterized reaching and grasping movements in 3D space.</a> Journal of Neuroscience Methods. 312, 139-147 (2019).</li><li>Zhang, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+an+invasive+brain-machine+interface+with+a+monkey+model.">Development of an invasive brain-machine interface with a monkey model.</a> Chinese Science Bulletin. 57 (16), 2036 (2012).</li><li>Hao, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distinct+neural+patterns+enable+grasp+types+decoding+in+monkey+dorsal+premotor+cortex.">Distinct neural patterns enable grasp types decoding in monkey dorsal premotor cortex.</a> Journal of Neural Engineering. 11 (6), 066011 (2014).</li></ol>

The authors have nothing to disclose.

The behavioral apparatus is described here enables a trial-wise combination of different reaching and grasping movements (i.e., the monkey can grasp differently shaped objects in any arbitrary 3D locations in each trial). This is accomplished through the combination of a custom turning table that switches different objects and a linear translational device that transports the turning table to multiple positions in 3D space. In addition, the neural signals from the monkey, trajectory of wrist, and hand shapes were able to...

Various apparatuses have been developed to study the neural principles underlying reaching and grasping movement in non-human primate. In reaching tasks, touch screen1,2, screen cursor controlled by a joystick3,4,5,6,7, and virtual reality technology8,9,10 have all been employed to present 2D and 3D targets, respectively. To introduce different grip types, differently shaped objects fixed in one position or rotating around an axis were widely used in the grasping tasks11,12,13. An alternative is to use visual cues to inform subjects to grasp the same object with different grip types14,15,16,17. More recently, reaching and grasping movements have been studied together (i.e., subjects reach multiple positions and grasp with different grip types in an experimental session)18,19,20,21,22,23,24,25,26,27,28,29. Early experiments have presented objects manually, which inevitably lead to low time and spatial precision20,21. To improve experimental precision and save manpower, automatic presentation devices controlled by programs have been widely used. To vary the target position and grip type, experimenters have exposed multiple objects simultaneously, but the relative (or absolute) position of targets and the grip types are bound together, which causes rigid firing patterns through long-term training22,27,28. Objects are usually presented in a 2D plane, which limits the diversity of reaching movement and neural activity19,25,26. Recently, virtual reality24 and robot arm23,29 have been introduced to present objects in 3D space.
Presented here are detailed protocols for building and using an automated apparatus30 that can achieve any combination of multiple target positions and grip types in 3D space. We designed a turning table to switch objects and 3D translational device to transport the turning table in 3D space. Both the turning table and translational device are driven by independent motors. Meanwhile, the 3D trajectory of subject&rsquo;s wrist and neural signals are recorded simultaneously throughout the experiment. The apparatus provides a valuable platform for the study of upper limb function in the rhesus monkey.

<table>
	<tbody>
		<tr>
			<td>Active X-rail</td>
			<td>CCM Automation technology Inc., China</td>
			<td>W50-25</td>
			<td>Effective travel, 600 mm; Load, 25 kg</td>
		</tr>
		<tr>
			<td>Active Y-rail</td>
			<td>CCM Automation technology Inc., China</td>
			<td>W60-35</td>
			<td>Effective travel, 300 mm, Load 35 kg</td>
		</tr>
		<tr>
			<td>Active Z-rail</td>
			<td>CCM Automation technology Inc., China</td>
			<td>W50-25</td>
			<td>Effective travel, 500 mm; Load 25 kg</td>
		</tr>
		<tr>
			<td>Bearing</td>
			<td>Taobao.com</td>
			<td>6004-2RSH</td>
			<td>Acrylic</td>
		</tr>
		<tr>
			<td>Case</td>
			<td>Custom mechanical processing</td>
			<td>TT-C</td>
			<td>Acrylic</td>
		</tr>
		<tr>
			<td>Connecting ring</td>
			<td>CCM Automation technology Inc., China</td>
			<td>57/60-W50</td>
			<td></td>
		</tr>
		<tr>
			<td>Connecting shaft</td>
			<td>CCM Automation technology Inc., China</td>
			<td>D12-700</td>
			<td>Diam., 12 mm;Length, 700 mm</td>
		</tr>
		<tr>
			<td>Diaphragm coupling</td>
			<td>CCM Automation technology Inc., China</td>
			<td>CCM 12-12</td>
			<td>Inner diam., 12-12mm</td>
		</tr>
		<tr>
			<td>Diaphragm coupling</td>
			<td>CCM Automation technology Inc., China</td>
			<td>CCM 12-14</td>
			<td>Inner diam., 14-12mm</td>
		</tr>
		<tr>
			<td>Electric slip ring</td>
			<td>Semring Inc., China</td>
			<td>SNH020a-12</td>
			<td>Acrylic</td>
		</tr>
		<tr>
			<td>Locating bar</td>
			<td>Custom mechanical processing</td>
			<td>TT-L</td>
			<td>Acrylic</td>
		</tr>
		<tr>
			<td>Motion capture system</td>
			<td>Motion Analysis Corp. US</td>
			<td>Eagle-2.36</td>
			<td></td>
		</tr>
		<tr>
			<td>Neural signal acquisition system</td>
			<td>Blackrock Microsystems Corp. US</td>
			<td>Cerebus</td>
			<td></td>
		</tr>
		<tr>
			<td>NI DAQ device</td>
			<td>National Instruments, US</td>
			<td>USB-6341</td>
			<td></td>
		</tr>
		<tr>
			<td>Object</td>
			<td>Custom mechanical processing</td>
			<td>TT-O</td>
			<td>Acrylic</td>
		</tr>
		<tr>
			<td>Passive Y-rail</td>
			<td>CCM Automation technology Inc., China</td>
			<td>W60-35</td>
			<td>Effective travel, 300 mm; Load 35 kg</td>
		</tr>
		<tr>
			<td>Passive Z-rail</td>
			<td>CCM Automation technology Inc., China</td>
			<td>W50-25</td>
			<td>Effective travel, 500 mm; Load 25 kg</td>
		</tr>
		<tr>
			<td>Pedestal</td>
			<td>CCM Automation technology Inc., China</td>
			<td>80-W60</td>
			<td></td>
		</tr>
		<tr>
			<td>Peristaltic pump</td>
			<td>Longer Inc., China</td>
			<td>BT100-1L</td>
			<td></td>
		</tr>
		<tr>
			<td>Planetary gearhead</td>
			<td>CCM Automation technology Inc., China</td>
			<td>PLF60-5</td>
			<td>Flange, 60&#215;60 mm; Reduction ratio, 1:5</td>
		</tr>
		<tr>
			<td>Right triangle frame</td>
			<td>CCM Automation technology Inc., China</td>
			<td>290-300</td>
			<td></td>
		</tr>
		<tr>
			<td>Rotator</td>
			<td>Custom mechanical processing</td>
			<td>TT-R</td>
			<td>Acrylic</td>
		</tr>
		<tr>
			<td>Servo motor</td>
			<td>Yifeng Inc., China</td>
			<td>60ST-M01930</td>
			<td>Flange, 60&#215;60 mm; Torque, 1.91 N&#183;m; for Y- and Z-rail</td>
		</tr>
		<tr>
			<td>Servo motor</td>
			<td>Yifeng Inc., China</td>
			<td>60ST-M01330</td>
			<td>Flange, 60&#215;60 mm; Torque, 1.27 N&#183;m; for X-rail</td>
		</tr>
		<tr>
			<td>Shaft</td>
			<td>Custom mechanical processing</td>
			<td>TT-S</td>
			<td>Acrylic</td>
		</tr>
		<tr>
			<td>Stepping motor</td>
			<td>Taobao.com</td>
			<td>86HBS120</td>
			<td>Flange, 86&#215;86 mm; Torque, 1.27 N&#183;m; Driving turning table</td>
		</tr>
		<tr>
			<td>Touch sensor</td>
			<td>Taobao.com</td>
			<td>CM-12X-5V</td>
			<td></td>
		</tr>
		<tr>
			<td>Tricolor LED</td>
			<td>Taobao.com</td>
			<td>CK017, RGB</td>
			<td></td>
		</tr>
		<tr>
			<td>T-shaped connecting board</td>
			<td>CCM Automation technology Inc., China</td>
			<td>110-120</td>
			<td></td>
		</tr>
	</tbody>
</table>

design and use of an apparatus for presenting graspable objects in 3d workspace

Reaching and grasping are highly-coupled movements, and their underlying neural dynamics have been widely studied in the last decade. To distinguish reaching and grasping encodings, it is essential to present different object identities independent of their positions. Presented here is the design of an automatic apparatus that is assembled with a turning table and three-dimensional (3D) translational device to achieve this goal. The turning table switches different objects corresponding to different grip types while the 3D translational device transports the turning table in 3D space. Both are driven independently by motors so that the target position and object are combined arbitrarily. Meanwhile, wrist trajectory and grip types are recorded via the motion capture system and touch sensors, respectively. Furthermore, representative results that demonstrate successfully trained monkey using this system are described. It is expected that this apparatus will facilitate researchers to study kinematics, neural principles, and brain-machine interfaces related to upper limb function.

The size of complete workspace of the apparatus is 600 mm, 300 mm, and 500&#8201;mm in x-, y-, and z-axes, respectively. The maximum load of the 3D translational device is 25 kg, while the turning table (including the stepping motor) is weighted 15&#8201;kg and can be transported at a speed of up to 500&#8201;mm/s. The kinematic precision of the 3D translational device is less than 0.1&#8201;mm and the noise of the apparatus is less than 60 dB.
To demonstrate the utility of the system, the mon...

Watch this Scientific Journal Video about Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace at JoVE.com

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Presented here is a protocol to build an automatic apparatus that guides a monkey to perform the flexible reach-to-grasp task. The apparatus combines a 3D translational device and turning table to present multiple objects in an arbitrary position in 3D space.

Reaching and grasping are highly-coupled movements, and their underlying neural dynamics have been widely studied in the last decade. To distinguish ...

design-use-an-apparatus-for-presenting-graspable-objects-3d

Research

JoVE Journal

Behavior

5.7K Views.  Zhejiang University. Presented here is a protocol to build an automatic apparatus that guides a monkey to perform the flexible reach-to-grasp task. The apparatus combines a 3D translational device and turning table to present multiple objects in an arbitrary position in 3D space.

Video: Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm (the visual oddball task) with a long history of behavioral and electroencephalography (EEG) experiments to a functional magnetic resonance imaging (fMRI) experiment is considered. The aims of this paper are to briefly describe fMRI and when its use is appropriate in cognitive neuroscience; illustrate how task design can influence the results of an fMRI experiment, particularly when that task is borrowed from another imaging modality; explain the practical aspects of performing an fMRI experiment. It is demonstrated that manipulating the task demands in the visual oddball task results in different patterns of blood oxygen level dependent (BOLD) activation. The nature of the fMRI BOLD measure means that many brain regions are found to be active in a particular task. Determining the functions of these areas of activation is very much dependent on task design and analysis. The complex nature of many fMRI tasks means that the details of the task and its requirements need careful consideration when interpreting data. The data show that this is particularly important in those tasks relying on a motor response as well as cognitive elements and that covert and overt responses should be considered where possible. Furthermore, the data show that transferring an EEG paradigm to an fMRI experiment needs careful consideration and it cannot be assumed that the same paradigm will work equally well across imaging modalities. It is therefore recommended that the design of an fMRI study is pilot tested behaviorally to establish the effects of interest and then pilot tested in the fMRI environment to ensure appropriate design, implementation and analysis for the effects of interest.

Transferring a paradigm with a history of use in EEG experiments to an fMRI experiment is considered. It is demonstrated that manipulating the task demands in the visual oddball task resulted in different patterns of BOLD activation and illustrated how task design is crucial in fMRI experiments.

As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm ...

Assessment of Murine Exercise Endurance Without the Use of a Shock Grid: An Alternative to Forced Exercise

Using laboratory mouse models, the molecular pathways responsible for the metabolic benefits of endurance exercise are beginning to be defined. The most common method for assessing exercise endurance in mice utilizes forced running on a motorized treadmill equipped with a shock grid. Animals who quit running are pushed by the moving treadmill belt onto a grid that delivers an electric foot shock; to escape the negative stimulus, the mice return to running on the belt. However, avoidance behavior and psychological stress due to use of a shock apparatus can interfere with quantitation of running endurance, as well as confound measurements of postexercise serum hormone and cytokine levels. Here, we demonstrate and validate a refined method to measure running endurance in na&#239;ve C57BL/6 laboratory mice on a motorized treadmill without utilizing a shock grid. When mice are preacclimated to the treadmill, they run voluntarily with gait speeds specific to each mouse. Use of the shock grid is replaced by gentle encouragement by a human operator using a tongue depressor, coupled with sensitivity to the voluntary willingness to run on the part of the mouse. Clear endpoints for quantifying running time-to-exhaustion for each mouse are defined and reflected in behavioral signs of exhaustion such as splayed posture and labored breathing. This method is a humane refinement which also decreases the confounding effects of stress on experimental parameters.

A method to assess exercise endurance in laboratory mice without the use of a shock grid is demonstrated. This method is a humane refinement that can decrease the confounding effects of stress on experimental parameters.

Using laboratory mouse models, the molecular pathways responsible for the metabolic benefits of endurance exercise are beginning to be defined. The most ...

Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression

Ruminative brooding is associated with increased vulnerability to major depression. Individuals who regularly ruminate will often try to reduce the frequency of their negative thoughts by actively suppressing them. We aim to identify the neural correlates underlying thought suppression in at-risk and depressed individuals. Three groups of women were studied; a major depressive disorder group, an at-risk group (having a first degree relative with depression) and controls. Participants performed a mixed block-event fMRI paradigm involving thought suppression, free thought and motor control periods. Participants identified the re-emergence of &#8220;to-be-suppressed&#8221; thoughts (&#8220;popping&#8221; back into conscious awareness) with a button press. During thought suppression the control group showed the greatest activation of the dorsolateral prefrontal cortex, followed by the at-risk, then depressed group. During the re-emergence of intrusive thoughts compared to successful re-suppression of those thoughts, the control group showed the greatest activation of the anterior cingulate cortices, followed by the at-risk, then depressed group. At-risk participants displayed anomalies in the neural regulation of thought suppression resembling the dysregulation found in depressed individuals. The predictive value of these changes in the onset of depression remains to be determined.

We aim to identify the neural correlates underlying sustained and transient thought suppression, and thought re-emergence in controls, at-risk and depressed individuals. Activation was greatest for controls compared to the at-risk and the depressed group in the dorsolateral prefrontal cortex during thought suppression and anterior cingulate cortex during thought re-emergence.

Ruminative brooding is associated with increased vulnerability to major depression. Individuals who regularly ruminate will often try to reduce the ...

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in  Free Moving Rodents

Many neurological and psychiatric patients demonstrate difficulties and/or deficits in decision making. Rodent models are helpful to produce a deeper understanding of the neurobiological causes underlying the decision-making problems. A cost-benefit based T-maze task is used for measuring decision making in which rodents choose between a high reward arm (HRA) and a low reward arm (LRA). There are two paradigms of the T-maze decision-making task, one in which the cost is a time delay and the other in which it is physical effort. Both paradigms require a tedious and labor-intensive management of experimental animals, multiple doors, pellet reward, and arm choice recordings. In the current work, we invented an apparatus based on traditional T-maze with full automation for pellet delivery, door management and choice recordings. This automated setup can be used for the evaluation of both delay- and effort-based decision making in rodents. With the protocol described here, our lab investigated the decision-making phenotypes of multiple genetically modified mice. In the representative data, we showed that the mice with ablated medial habenular showed aversions of both delay and effort and tended to choose the immediate and effortless reward. This protocol helps to decrease the variability caused by experimenter intervention and to enhance experiment efficiency. In addition, chronic silicon probe or microelectrode recording, fiber-optic imaging and/or manipulation of neural activity can be easily applied during the decision-making task using the setup described here.

This article introduces an automated T-maze apparatus that we invented, and a protocol based on this apparatus for analyzing delay-based decision making and effort-based decision making in free moving rodents.

Many neurological and psychiatric patients demonstrate difficulties and/or deficits in decision making. Rodent models are helpful to produce a deeper ...

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

The utility of three-dimensional (3D) kinematic motion analysis systems is limited in rodents. Part of the reason for this inadequacy is the use of complex algorithms and mathematical modeling that accompany 3D data collection and analysis procedures. This work provides a simple, user-friendly, step-by-step detailed methodology for 3D kinematic gait analysis during treadmill locomotion in healthy and neurotraumatic rats using a six-camera motion capture system. Also provided are details on 1) calibration of the system in an experimental set-up customized for quadrupedal locomotion, 2) data collection for treadmill locomotion in adult rats using markers positioned on all four limbs, 3) options available for video tracking and processing, and 4) basic 3D kinematic data generation and visualization and quantification of data using the built-in data collection software. Finally, it is suggested that the utility of this motion capture system be expanded to studying a variety of motor behaviors before and after neurotrauma.

Presented here is a protocol to collect and analyze three-dimensional kinematics of quadrupedal locomotion in rodents for preclinical studies.

The utility of three-dimensional (3D) kinematic motion analysis systems is limited in rodents. Part of the reason for this inadequacy is the use of ...

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effective. Here, we describe a protocol that uses machine learning techniques to rapidly select candidates for further screening via a question-based MCI test. This minimizes the number of resources required for screening because only patients who are potentially MCI positive are tested further.
This methodology was applied in an initial MCI research study that formed the starting point for the design of a selective screening decision tree. The initial study collected many demographic and lifestyle variables as well as details about patient medications. The Short Portable Mental Status Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE) were used to detect possible cases of MCI. Finally, we used this method to design an efficient process for classifying individuals at risk of MCI. This work also provides insights into lifestyle-related factors associated with MCI that could be leveraged in the prevention and early detection of MCI among elderly populations.

This methodology produces decision trees that target population groups more prone to suffering from mild cognitive impairment and are useful for cost-effective selective screening of the disease.

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common ...

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Our knowledge of human object vision is based almost exclusively on studies in which the stimuli are presented in the form of computerized two-dimensional (2-D) images. In everyday life, however, humans interact predominantly with real-world solid objects, not images. Currently, we know very little about whether images of objects trigger similar behavioral or neural processes as do real-world exemplars. Here, we present methods for bringing the real-world into the laboratory. We detail methods for presenting rich, ecologically-valid real-world stimuli under tightly-controlled viewing conditions. We describe how to match closely the visual appearance of real objects and their images, as well as novel apparatus and protocols that can be used to present real objects and computerized images on successively interleaved trials. We use a decision-making paradigm as a case example in which we compare willingness-to-pay (WTP) for real snack foods versus 2-D images of the same items. We show that WTP increases by 6.6% for food items displayed as real objects versus high-resolution 2-D colored&#160;images of the same foods -suggesting that real foods are perceived as being more valuable than their images. Although presenting real object stimuli under controlled conditions presents several practical challenges for the experimenter, this approach will fundamentally expand our understanding of the cognitive and neural processes that underlie naturalistic vision.

We describe methods for presenting real-world objects and matched images of the same objects under tightly-controlled experimental conditions. The methods are described in the context of a decision-making task, but the same real-world approach can be extended to other cognitive domains such as perception, attention, and memory.

Our knowledge of human object vision is based almost exclusively on studies in which the stimuli are presented in the form of computerized two-dimensional ...

Transcranial Direct Current Stimulation for Online Gamers

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that applies a weak electric current to the scalp to modulate neuronal membrane potentials. Compared to other brain stimulation methods, tDCS is relatively safe, simple, and inexpensive to administer.
Since excessive online gaming can negatively affect mental health and daily functioning, developing treatment options for gamers is necessary. Although tDCS over the dorsolateral prefrontal cortex (DLPFC) has demonstrated promising results for various addictions, it has not been tested in gamers. This paper describes a protocol and a feasibility study for applying repeated tDCS over the DLPFC and neuroimaging to examine the underlying neural correlates in gamers.
At baseline, individuals who play online games report average weekly hours spent on games, complete questionnaires on addiction symptoms and self-control, and undergo brain 18F-&#64258;uoro-2-deoxyglucose positron emission tomography (FDG-PET). The tDCS protocol consists of 12 sessions over the DLPFC for 4 weeks (anode F3/cathode F4, 2 mA for 30 min per session). Then, a follow-up is conducted using the same protocol as the baseline. Individuals who do not play online games receive only baseline FDG-PET scans without tDCS. Changes of clinical characteristics and asymmetry of regional cerebral metabolic rate of glucose (rCMRglu) in the DLPFC are examined in gamers. In addition, asymmetry of rCMRglu is compared between gamers and non-gamers at baseline.
In our experiment, 15 gamers received tDCS sessions and completed baseline and follow-up scans. Ten non-gamers underwent FDG-PET scans at the baseline. The tDCS reduced addiction symptoms, time spent on games, and increased self-control. Moreover, abnormal asymmetry of rCMRglu in the DLPFC at baseline was alleviated after tDCS.
The current protocol may be useful for assessing treatment efficacy of tDCS and its underlying brain changes in gamers. Further randomized sham-controlled studies are warranted. Moreover, the protocol can be applied to other neurological and psychiatric disorders.

We present a protocol and a feasibility study for applying transcranial direct current stimulation (tDCS) and neuroimaging assessment in online gamers.

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that applies a weak electric current to the scalp to modulate ...

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Current athlete monitoring practice in team sports is mainly based on positional data measured by global positioning or local positioning systems. The disadvantage of these measurement systems is that they do not register lower extremity kinematics, which could be a useful measure for identifying injury-risk factors. Rapid development in sensor technology may overcome the limitations of the current measurement systems. With inertial measurement units (IMUs) securely fixed to body segments, sensor fusion algorithms and a biomechanical model, joint kinematics could be estimated. The main purpose of this article is to demonstrate a sensor setup for estimating hip and knee joint kinematics of team sport athletes in the field. Five male subjects (age 22.5 &#177; 2.1 years; body mass 77.0 &#177; 3.8 kg; height 184.3 &#177; 5.2 cm; training experience 15.3 &#177; 4.8 years) performed a maximal 30-meter linear sprint. Hip and knee joint angles and angular velocities were obtained by five IMUs placed on the pelvis, both thighs and both shanks. Hip angles ranged from 195&#176; (&#177; 8&#176;) extension to 100.5&#176; (&#177; 8&#176;) flexion and knee angles ranged from 168.6&#176; (&#177; 12&#176;) minimal flexion and 62.8&#176; (&#177; 12&#176;) maximal flexion. Furthermore, hip angular velocity ranged between 802.6 &#176;&#183;s-1 (&#177; 192 &#176;&#183;s-1) and -674.9 &#176;&#183;s-1 (&#177; 130 &#176;&#183;s-1). Knee angular velocity ranged between 1155.9 &#176;&#183;s-1 (&#177; 200 &#176;&#183;s-1) and -1208.2 &#176;&#183;s-1 (&#177; 264 &#176;&#183;s-1). The sensor setup has been validated and could provide additional information with regard to athlete monitoring in the field.&#160;This may help professionals in a daily sports setting to evaluate their training programs, aiming to reduce injury and optimize performance.

Monitoring athletes is essential for improving performance and reducing injury risk in team sports. Current methods to monitor athletes do not include the lower extremities. Attaching multiple inertial measurement units to the lower extremities could improve monitoring athletes in the field.

Current athlete monitoring practice in team sports is mainly based on positional data measured by global positioning or local positioning systems. The ...

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Collision warning system plays a key role in the prevention of driving distractions and drowsy driving. Previous studies have proven the advantages of tactile warnings in reducing driver&#8217;s brake response time. At the same time, tactile warnings have been proved effective in take-over request (TOR) for partially autonomous vehicles.
How the performance of tactile warnings can be optimized is an ongoing hot research topic in this field. Thus, the presented low-cost driving simulation software and methods are introduced to attract more researchers to take part in the investigation. The presented protocol has been divided into five sections: 1) participants, 2) driving simulation software configuration, 3) driving simulator preparation, 4) vibrating toolkit configuration and preparation, and 5) conducting the experiment.
In the exemplar study, participants wore the tactile vibrating toolkit and performed an established car-following task using the customized driving simulation software. The front vehicle braked intermittently, and vibrating warnings were delivered whenever the front vehicle was braking. Participants were instructed to respond as quickly as possible to the sudden brakes of the front vehicle. Driving dynamics, such as the brake response time and brake response rate, were recorded by the simulation software for data analysis.
The presented protocol offers insight into the exploration of the effectiveness of tactile warnings on different body locations. In addition to the car-following task that is demonstrated in the exemplar experiment, this protocol also provides options&#160;to apply other paradigms to the driving simulation studies by making simple software configuration without any code development. However, it is important to note that due to its affordable price, the driving simulation software and hardware introduced here may not be able to fully compete with other high-fidelity commercial driving simulators. Nevertheless, this protocol can act as an affordable and user-friendly alternative to the general high-fidelity commercial driving simulators.

This protocol describes a driving simulation platform and a tactile vibrating toolkit for the investigation of driving-related research. An exemplar experiment exploring the effectiveness of tactile warnings is also presented.&#160;

Collision warning system plays a key role in the prevention of driving distractions and drowsy driving. Previous studies have proven the advantages of ...