This work was supported in part by the National Institutes of Health under Grant RHD064968A, in part by the National Science Foundation under Grant 0931820, Grant 1149385, and Grant 1361549, and in part by the National Institute on Disability and Rehabilitation Research under Grant H133G120165. The authors thank Lin Du, Ding Wang and Gerald Hefferman at the University of Rhode Island, and Michael J. Nunnery at the Nunnery Orthotic and Prosthetic Technology, LLC, for their great suggestion and assistance in this study.

1. Platform for Implementation of Neural Control of Powered Transfemoral Prostheses
An engineering platform was developed in this study to implement and evaluate neural control of powered artificial legs. The hardware included a desktop PC with 2.8 GHz CPU and 4 GB RAM, a multi-functional data acquisition board with both analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), a motor controller, digital I/Os, and a prototypical powered transfemoral prosthesis designed in our...

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	<li>Martinez-Villalpando, E. C., Herr, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Agonist-antagonist+active+knee+prosthesis:+a+preliminary+study+in+level-ground+walking.">Agonist-antagonist active knee prosthesis: a preliminary study in level-ground walking.</a> J Rehabil Res Dev. 46, 361-373 (2009).</li><li>Sup, F., Bohara, A., Goldfarb, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+and+Control+of+a+Powered+Transfemoral+Prosthesis.">Design and Control of a Powered Transfemoral Prosthesis.</a> Int J Rob Res. 27, 263-273 (2008).</li><li>Au, S., Berniker, M., Herr, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Powered+ankle-foot+prosthesis+to+assist+level-ground+and+stair-descent+gaits.">Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits.</a> Neural Netw. 21, 654-666 (2008).</li><li>Hargrove, L. J., Simon, A. M., Lipschutz, R. D., Finucane, S. B., Kuiken, T. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real-time+myoelectric+control+of+knee+and+ankle+motions+for+transfemoral+amputees.">Real-time myoelectric control of knee and ankle motions for transfemoral amputees.</a> JAMA. 305, 1542-1544 (2011).</li><li>Ha, K. H., Varol, H. A., Goldfarb, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Volitional+control+of+a+prosthetic+knee+using+surface+electromyography.">Volitional control of a prosthetic knee using surface electromyography.</a> IEEE Trans Biomed Eng. 58, 144-151 (2011).</li><li>Huang, H., Kuiken, T. A., Lipschutz, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+strategy+for+identifying+locomotion+modes+using+surface+electromyography.">A strategy for identifying locomotion modes using surface electromyography.</a> IEEE Trans Biomed Eng. 56, 65-73 (2009).</li><li>Huang, 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=Continuous+Locomotion+Mode+Identification+for+Prosthetic+Legs+based+on+Neuromuscular-Mechanical+Fusion.">Continuous Locomotion Mode Identification for Prosthetic Legs based on Neuromuscular-Mechanical Fusion.</a> IEEE Trans Biomed Eng. 58, 2867-2875 (2011).</li><li>Zhang, F., Dou, Z., Nunnery, M., Huang, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real-time+implementation+of+an+intent+recognition+system+for+artificial+legs.">Real-time implementation of an intent recognition system for artificial legs.</a> Conf Proc IEEE Eng Med Biol Soc. 2011, 2997-3000 (2011).</li><li>Zhang, F., Huang, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Source+Selection+for+Real-time+User+Intent+Recognition+towards+Volitional.">Source Selection for Real-time User Intent Recognition towards Volitional.</a> Control of Artificial Legs IEEE Journal of Biomedical and Health Informatics. PP, (2013).</li><li>Liu, M., Datseris, P., Huang, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+prototype+for+smart+prosthetic+legs:+analysis+and+mechanical+design.">A prototype for smart prosthetic legs: analysis and mechanical design.</a> Proceedings of the International Conference on Control, Robotics and Cybernetics. , 139-143 (2011).</li></ol>

An engineering platform was developed in this study to easily implement, optimize, and develop true neural control of powered prostheses. The whole platform was programmed in a virtual instrumentation based development environment and implemented on a desktop PC. The control software was composed of several independent and interchangeable modules, in each of which a specific functionality was executed (i.e. NMI intent recognition, and intrinsic control). The advantage of this modular design is that each function...

Powered lower limb prostheses have gained increasing attention in both commercial market1,2 and research community3-5. Compared to traditional passive prosthetic legs, motorized prosthetic joints have the advantage of allowing lower limb amputees to more efficiently perform activities that are difficult or impossible when wearing passive devices. However, currently, smooth and seamless activity transition (e.g., from level-ground walking to stair ascent) is still a challenging issue for powered prosthetic leg users. This difficulty is mainly due to the lack of a user-machine interface that can &#8220;read&#8221; the user&#8217;s movement intent and promptly adjust prosthesis control parameters in order to enable the users to seamlessly switch the activity mode.
To address these challenges, various approaches in designing user-machine interface have been explored. Wherein NMI based on electromyographic (EMG) signals has demonstrated a great potential to allow intuitive control of powered lower limb prostheses. Two recent studies6,7 reported decoding the intended motion of the missing knee of transfemoral amputees by monitoring the EMG signals recorded from residual muscles during a seated position. Au et al.5 used EMG signals measured from residual shank muscles to identify two locomotion modes (level-ground walking and stair descent) of one transtibial amputee. Huang et al.8 proposed a phase-dependent EMG pattern recognition approach that can recognize seven activity modes with approximately 90% accuracy as demonstrated on two transfemoral amputees. To better improve the intent-recognition performance, a NMI based on neuromuscular-mechanical fusion was designed in our group9 and online evaluated on transfemoral amputees wearing passive prosthetic legs for intent recognition10,11. This NMI can accurately identify the user&#8217;s intended activities and predict the activity transitions9, which was potentially useful for neural control of powered artificial legs.
The current question facing us is how to integrate our NMI into the prosthesis control system in order to enable intuitive prosthesis operation and ensure the user&#8217;s safety. Developing true neurally-controlled artificial legs requires a flexible platform in the laboratory for easy implementation and optimization of prosthesis control algorithms. Therefore, the objective of this study is to report a flexible engineering platform developed in our lab for testing and optimizing the prosthesis control algorithms. In addition, new experimental setup and protocol are presented for evaluating the neurally-controlled powered transfemoral prostheses on patients with lower limb amputations safely and efficiently. The platform and experimental design presented in this study could benefit the future development of true neurally-controlled, powered artificial legs.

<table border="0" cellpadding="0" cellspacing="0" width="823">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:248px;">Trigno Wireless EMG Sensors</td>
			<td style="width:221px;">Delsys, Inc.</td>
			<td style="width:143px;">7</td>
			<td style="width:211px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:248px;">Trigno Wireless EMG Base Station</td>
			<td style="width:221px;">Delsys, Inc.</td>
			<td style="width:143px;">1</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:248px;">Multi-functional DAQ card (PCI-6259)</td>
			<td style="width:221px;">National Instruments, Inc.</td>
			<td style="width:143px;">1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:248px;">Potentiometer (RDC503013A)</td>
			<td style="width:221px;">ALPS Electric CO., LTD</td>
			<td style="width:143px;">1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:248px;">Encoder (MR series)</td>
			<td style="width:221px;">Maxon Precision Motors, Inc.</td>
			<td style="width:143px;">1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Motor controller (ADS50/10)&#160;</td>
			<td style="width:221px;">Maxon Precision Motors, Inc.</td>
			<td style="width:143px;">1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:248px;">24 V Power Supply (DPP480)</td>
			<td style="width:221px;">TDK-Lambda Americas, Inc.</td>
			<td style="width:143px;">1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:248px;">6 DOF Load Cell (Mini58)</td>
			<td style="width:221px;">ATI Industrial Automation</td>
			<td style="width:143px;">1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:248px;">Ceiling Rail System</td>
			<td style="width:221px;">RoMedic, Inc.</td>
			<td style="width:143px;">1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:248px;">NI LabView 2011</td>
			<td style="width:221px;">National Instruments, Inc.</td>
			<td style="width:143px;">1</td>
			<td></td>
		</tr>
	</tbody>
</table>

engineering platform and experimental protocol for design and evaluation of a neurally-controlled powered transfemoral prosthesis

To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is desired. A novel neural-machine interface (NMI) based on neuromuscular-mechanical fusion developed in our previous study has demonstrated a great potential to accurately identify the intended movement of transfemoral amputees. However, this interface has not yet been integrated with a powered prosthetic leg for true neural control. This study aimed to report (1) a flexible platform to implement and optimize neural control of powered lower limb prosthesis and (2) an experimental setup and protocol to evaluate neural prosthesis control on patients with lower limb amputations. First a platform based on a PC and a visual programming environment were developed to implement the prosthesis control algorithms, including NMI training algorithm, NMI online testing algorithm, and intrinsic control algorithm. To demonstrate the function of this platform, in this study the NMI based on neuromuscular-mechanical fusion was hierarchically integrated with intrinsic control of a prototypical transfemoral prosthesis. One patient with a unilateral transfemoral amputation was recruited to evaluate our implemented neural controller when performing activities, such as standing, level-ground walking, ramp ascent, and ramp descent continuously in the laboratory. A novel experimental setup and protocol were developed in order to test the new prosthesis control safely and efficiently. The presented proof-of-concept platform and experimental setup and protocol could aid the future development and application of neurally-controlled powered artificial legs.

Figure 4a shows seven channels of surface EMG signals measured from the thigh muscles of the subject&#8217;s residual limb when he performed hip flexion/extension, as described in Protocol 3.2.6. Figure 4b shows six gait cycles of EMG signals recorded when the subject walked on a level-ground walking path, during Protocol 3.3.4. From this figure, it can be seen that the new designed EMG electrode-socket interface can provide good quality of surface EMG signal measurements.
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Watch this Scientific Journal Video about Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis at JoVE.com

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Neural-machine interfaces (NMI) have been developed to identify the user's locomotion mode. These NMIs are potentially useful for neural control of powered artificial legs, but have not been fully demonstrated. This paper presented (1) our designed engineering platform for easy implementation and development of neural control for powered lower limb prostheses and (2) an experimental setup and protocol in a laboratory environment to evaluate neurally-controlled artificial legs on patients with lower limb amputations safely and efficiently.

To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is ...

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16.2K visualizações.  North Carolina State University & University of North Carolina at Chapel Hill.  Interfaces de Neural-máquina (MNI) foram desenvolvidos para identificar o modo de locomoção do usuário. Estes NMIs são potencialmente úteis para o controle neural de pernas artificiais alimentados, mas não têm sido plenamente demonstrada. Este artigo apresentou (1) a nossa plataforma de engenharia projetada para fácil implementação e desenvolvimento do controle neural para próteses de membros inferiores com alimentação e (2) uma montagem experimental e prot...