The authors wish to thank Mr Hans Oppel and his prosthetic technicians of Otto Bock Healthcare Products GmbH for manufacturing the socket used by the participant in this study. This study was financially supported by the European Research Council (ERC) via the ERC Advanced Grant DEMOVE (No. 267888), the Austrian Council for Research and Technology Development, and the Austrian Federal Ministry of Science, Research &#38; Economy.

This study was carried out in accordance with the Declaration of Helsinki, as approved by the local research ethics committee. The study was explained in full detail to the participant prior to commencement, allowing the participant the time to weigh up the decision to voluntarily take part in the study and confirm his participation by informed, written consent.
Note: One man, aged 27 years, took part in the study. The participant had normal vision, was a left below-elbow amputee, and was an experienced user (4 years total prosthesis use). Prior to commencing this study the prosthesis he used on a daily basis was a 4 channel myoelectric prosthetic hand with wrist rotation for 12-15 hr per day for 15 months. The participant&#8217;s right hand had previously been surgically reconstructed, but had no other physical or neurological impairment.
1. Study Design
<ol>
	<li>Split the study over two sessions: na&#239;ve use, and use following structured training. 
	Note: This is to allow intra-subject comparison before and after training respectively.</li>
	<li>Ensure that these two sessions are at least three months apart, so as to be treated as independent from each other.</li>
	<li>At the beginning of both sessions, fit a customized socket and prosthesis to the participant. Ensure that the prosthetic hardware and control algorithms match those detailed in the Materials section of this protocol. Ensure that the participant is not able to use the customized prosthesis in the intervening time between sessions.</li>
	<li>Train the patient according the steps outlined in the Na&#239;ve Session and Structured Training Session sections of this protocol. At the beginning of each of these sessions, calibrate the prosthetic hardware. Use the collected calibration data for real-time prosthetic control.</li>
	<li>Once the Na&#239;ve Session and Structured Training Session are complete, assess the participant&#8217;s performance using the Southampton Hand Assessment Procedure (SHAP) outcome measure.23 Compare the SHAP scores to a baseline measure using the participant&#8217;s standard prosthesis (obtained before either training sessions).</li>
</ol>
2. Materials
<ol>
	<li>Fit the participant with a custom-built socket. Attach a commercially available prosthesis according to manufacturer&#8217;s instructions. Equip the prosthetic hand with prototype components that allow actuated wrist flexion, extension and rotation. This enables the participant to control the hand with 3.5 degrees of freedom (DoFs) (Table 1). 
	Note: In this experiment a Michaelangelo Hand (See Materials List) was used. Other terminal devices capable of wrist rotation, flexion and extension, together with standard grip functions would also be appropriate.</li>
	<li>Record EMG signals using eight equidistantly placed raw signal electrodes around the stump, and an on-board decoding system at the sampling rate of 1,000 Hz and digitized with 10-bit depth. Perform the initial filtering and amplification within the electrodes themselves according to the vendor&#8217;s specifications. Use a personal computer (PC) to conduct the main processing, which communicates with acquisition hardware and controls the prosthesis via a wireless connection. 
	Note: In this study the surface EMG electrodes and on-board decoding system (AxonBus) used were from Otto Bock. Other manufacturers of similar devices would also be appropriate. The wireless connection was via Bluetooth, and likewise other modalities could be applied.</li>
</ol>
3. Control Algorithm
<ol>
	<li>Use a control algorithm that provides simultaneous and proportional prosthetic control across multiple DoFs.21 The algorithm used in this study was a two-stage decision making paradigm, so that context-dependent movement estimation was possible.</li>
	<li>Upon system training, which contains all the controllable single DoF movements, record an incoming electromyogram (EMG).</li>
	<li>In the first stage, assess intrinsic dimensional information of the intended motion based on the Mahalanobis distance of the newly calculated EMG feature vector from the training data. Make a decision as to whether the user&#8217;s intention was to perform a fine 1-DoF or a coarser simultaneous 2-DoF motion. 
	Note: Mahalanobis distance of a feature vector x to class i with the class mean vector &#181;i and covariance matrix &#931;i is calculated as: 
	<img alt="Equation 1" src="/files/ftp_upload/52968/52968eq1.jpg" /> 
	As described in Amsuess et al, the newly calculated feature vector is mapped to the high dimensional space and the Mahalanobis distance of the transformed point to any of the trained class points is taken as the measure for novelty.21 An empirically determined threshold to that distance gives the decision for novelty (2-DOF) or not (1-DOF).</li>
	<li>In the second stage, based on the previous decision, use one of two parallel estimators &#8212; one dealing with the sequential movements (SEQ-E) and the other handling simultaneous motions (SIM-E) &#8212; to provide the control signals for the prosthesis. 
	Note: SEQ-E is in essence a proportional estimator (i.e., the strength of muscular contractions) based on common spatial patterns (CSP)21, while SIM-E is a linear regressor, which simultaneously steers 2 DoFs of the wrist.</li>
</ol>
4. Software Framework
Note: The software framework used in this study allowed handling of the communication between the prosthetic hardware and the embedded control algorithm. It also offered visually supportive training tools needed for maximizing participant training.
<ol>
	<li>Display the root mean square (RMS) of the EMG signals collected from the 8 equidistantly placed electrodes in a form of a polar plot of EMG amplitude as a function of electrode location. This visual feedback enables easy monitoring of the spatial distribution of the EMG in the transverse plane of the forearm. Using such a setup, each of the user&#8217;s motions can thereby elicit a distinct pattern22 in the polar plot, which can then be saved and used to train for the repeatability of the specific gesture. 
	Note: The framework enables the collection of EMG data in the standard pattern recognition manner.23 For each of the EMG channels the RMS over 40 msec is calculated as 
	<img alt="Equation 2" src="/files/ftp_upload/52968/52968eq2.jpg" /> 
	resulting in observations for every ensemble window.</li>
	<li>For initial calibration collect the maximal long-term voluntary contraction (MLVC) values for each intended motion. Prompt the participant by using the demonstrator&#8217;s hand to perform the desired motion while giving vocal and visual instructions for 5 sec.</li>
	<li>After calibration, present the participant with a set of trapezoidal cues. These force profiles contain plateaus set at 30%, 60% and 90% of the calibrated maximum.</li>
	<li>Within each trial, instruct the participant to steer the red pointer along the cue by modulating the force level of the prompted motion (Figure 1). The vertical position of the pointer corresponds to the summed RMS values across all eight channels. Set the duration of the trial to 5 sec with plateau interval corresponding to the middle 3 sec.</li>
</ol>
5. Na&#239;ve Session
Note: During the na&#239;ve training session, the participant had no prior experience of the prosthetic control scheme used in this study.
<ol>
	<li>Do not give the participant any formal clinical training, but only instruct that 8 actions of the residual limb, of which one is a resting state, will allow control of a visual target on a computer screen. These tasks are similar to those used in classic pattern recognition approaches for prosthetic control23, and for those methods the participant in this study had approximately 60 hr of previous experience.</li>
	<li>Display the required movements on screen in terms of text and a static picture while following a visual cue (Figure 1).</li>
	<li>Show the participant his EMG activation patterns, which correspond to eight specific and unique polar plots (Figure 2).</li>
	<li>Use audible instructions to encourage the participant to follow the visual cue. These audible instructions must be identical if used in the structured training session.</li>
	<li>Repeat the tasks three times with different arm positions (relaxed, reaching in front, reaching across) to enhance system training. Keep in mind that there are 8 different actions and three force levels, once all arm positions are covered, system training input sums up to the total of 72 individual samples.</li>
	<li>Once complete, allow the participant the opportunity to practice real-time control prior to completing the SHAP outcome assessment.</li>
	<li>Ensure the participant does not have access to the customized prosthesis and control algorithms beyond the end of the na&#239;ve session.</li>
</ol>
6. Structured Training Session
<ol>
	<li>Three months after the na&#239;ve session, perform a structured training session.</li>
	<li>Structure the session in the following ordered steps (Figure 3):
	<ol>
		<li>For imitation, instruct the participant to directly imitate the desired eight actions (Table 1) performed by the demonstrator in real-time. Execute each action for 3 sec.</li>
		<li>For repetition, ask the participant to repeat the action that has been imitated 10 times, so each action is performed for 30 sec.</li>
		<li>For reinforcement &#38; computer system training, ask the participant to now engage with the computer&#8217;s visual feedback, that is exactly the same setup as the Na&#239;ve session. Ensure that there is no difference between these two sections.</li>
		<li>For prosthetic control, ask the participant to practice real-time control of the customized prosthesis before completing the outcome assessment.</li>
	</ol>
	</li>
	<li>During imitation, seat the participant at a 45&#176; angle from the demonstrator and provide with a full and unobstructed view of the demonstrator&#8217;s hand matching the affected side of the participant during all actions (Figure 4). No visual cues from a computer screen should be available to the participant at this time.</li>
	<li>For repetition, during the participant&#8217;s actions, have the demonstrator observe the corresponding EMG activity as represented by the polar plots of each movement (Figure 4). Once the demonstrator has determined that the participant can produce unique and repeatable EMG activation patterns for each movement, ask the participant to repeat the actions for 30 sec without any visual cues. 
	Note: There are total of 8 unique actions &#8212; seven of them (wrist pronation/supination, wrist flexion/extension, hand open, key grip and fine pinch) requiring muscle activation, and the eighth being no action which represents a steady relaxed state.</li>
	<li>After reinforcement &#38; computer system training, present the participant with visual feedback of his eight actions, exactly as was seen in the na&#239;ve session, which correspond to the eight unique and specific polar plots on the computer screen (Figure 3). To tune performance, ask the participant to perform the actions while viewing the real-time polar plots with recorded motion overlays to reinforce learning, typically between 2-4 attempts for each movement. Once confident the participant can then complete the exact same tasks that were performed in the na&#239;ve session.</li>
</ol>
7. Prosthetic Control
<ol>
	<li>Use the training data sets from each session to calibrate and adjust the prosthesis for real time control.</li>
	<li>Initially, only allow the participant to control the prostheses by sequential proportional control, i.e., one movement at a time, with the speed of the device proportional to the levels of muscular contractions.</li>
	<li>Once each of the eight actions are performed in a repeatable and reliable manner, switch the control scheme to proportional and simultaneous control, allowing more than one movement of the wrist at a time.</li>
	<li>Have the participant practice simple tasks, such as picking up a bottle and laying it on its side (2 attempts is sufficient). Allow a period of rest before the outcome assessment is performed. In the case of this study, 2 hr of rest for the na&#239;ve session, and 24 hr of rest for the structured session.</li>
</ol>
8. Outcome Measurement
<ol>
	<li>Evaluate global upper extremity function both in the na&#239;ve and structured training sessions using the SHAP, which monitors hand and upper extremity function closely related to activities of daily living (ADLs). The tasks performed in the SHAP include manipulating light and heavy objects, as well as tasks of ADL such as cutting an object with a knife or undoing buttons. The SHAP has been validated for assessment of pathological and prosthetic hand function.24 
	Note: This measurement was chosen as the participant in this study had been routinely followed up with this outcome measure by his clinical team.</li>
</ol>

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The authors declare no competing financial interests.

Our findings suggest for the participant in this study that structured training helped improve control of a multifunctional prosthetic hand during one single session. The structured program used here was a combination of imitation, repetition and reinforcement of hand movements that the participant was not able to complete with his traditional prosthetic hand.
Although the participant scored higher with his traditional prosthesis in the SHAP test, it is worth noting that he typically wore that...

Replacing hand function in amputees is a difficult endeavor. Coordinating highly skilled hand movements is not an innate ability, and takes humans years of learning to develop.1&#8211;5 After the traumatic loss of a hand, replicating this capability by prosthetic means is not a trivial task and may require a period of sustained learning.
Prosthetic design and interfacing methods for their control are subject to rapid technological innovations, with the goal of multifunctional control in a natural manner.6 The complexity of these control systems increases substantially to provide more functions for amputees. To ensure accurate control of these systems, and to reduce abandonment of new technologies, adequate training needs to be established. This is likely to be more successful if it is based on the amputees&#8217; inherent learning strategies.
Vision can play an important role during learning of hand movements. Behavioral studies have shown that by observing the actions of others7 or using visual cues8, able-bodied individuals learn and coordinate new movements. Through a process of observation, understanding and execution of an observed action, individuals are able to imitate the actions of others. Specific cortical networks, which may include a mirror-neuron system (MNS), are believed to underlie this capability, and may have a role in controlling prosthetic limbs.9&#8211;11
The role of imitation might not just be limited to executing actions that have already been seen, but together with the MNS, allow the execution of movements that have not yet been observed but extrapolated from the observer&#8217;s motor repetoire.12 Indeed, imitation may not necessarily be an innate ability, but an accruement of motor skills over time that lead to experienced and sophisticated actions.13 The strength of observing actions, over just simply imagining them, has been shown to improve learning new tasks.14 Thus, imitation may be a pragmatic approach to training amputees, as evidence suggests it a goal directed process15, with the target in the rehabilitation setting of enabling useful prosthetic hand function.
Rehabilitation studies have separately shown that visual cues, such as virtual simulations of a prosthetic hand, encourage amputees during rehabilitation training.16 In addition, the use of repetition when conducted in a blocked paradigm has been shown to enable rapid learning of upper-limb prosthetic control.17 While virtual simulations have been proven to be equally effective as real control of prosthetic hands in enabling abled-body users to control myoelectric devices,18 their effect on amputees using standardized outcome measures is not clear. Finally, where protocols for upper limb amputation training exist, the role of imitation in learning of prosthetic control is not explicitly discussed.19,20
This study aims at understanding if the use of imitation, in combination with repetition and reinforcement, has a positive impact on the learning of multifunctional prosthetic control as part of a structured training program.
Presented herein is a case report of a transradial amputee who was trained to use a multifunctional prosthetic hand. The participant had previously become accustomed to operating traditional myoelectric prostheses. Using visual cues, both in the form of imitation of a healthy demonstrator and as simple computer visual feedback, the amputee quickly improved handling of his new device.

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	<tbody>
		<tr>
			<td>Michelangelo Hand</td>
			<td>Otto Bock Healthcare Products GmbH, A</td>
			<td>8E500=L-M</td>
			<td></td>
		</tr>
		<tr>
			<td>AxonRotation</td>
			<td>Otto Bock Healthcare Products GmbH, A</td>
			<td>9S503</td>
			<td></td>
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		<tr>
			<td>Wrist Flexor</td>
			<td>Otto Bock Healthcare Products GmbH, A</td>
			<td>&#160;--</td>
			<td>prototype unit</td>
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		<tr>
			<td>AxonMaster</td>
			<td>Otto Bock Healthcare Products GmbH, A</td>
			<td>13E500</td>
			<td></td>
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		<tr>
			<td>Electrode</td>
			<td>Otto Bock Healthcare Products GmbH, A</td>
			<td>13E200=50AC</td>
			<td></td>
		</tr>
		<tr>
			<td>ScissorFenceElectrodeCarrier</td>
			<td>Otto Bock Healthcare Products GmbH, A</td>
			<td>&#160;--</td>
			<td>prototype unit</td>
		</tr>
		<tr>
			<td>Acquisition Software</td>
			<td>Otto Bock Healthcare Products GmbH, A</td>
			<td>&#160;--</td>
			<td>prototype unit</td>
		</tr>
		<tr>
			<td>Carbon shaft</td>
			<td>Otto Bock Healthcare Products GmbH, A</td>
			<td>&#160;--</td>
			<td>prototype unit</td>
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a structured rehabilitation protocol for improved multifunctional prosthetic control: a case study

Advances in robotic systems have resulted in prostheses for the upper limb that can produce multifunctional movements. However, these sophisticated systems require upper limb amputees to learn complex control schemes. Humans have the ability to learn new movements through imitation and other learning strategies. This protocol describes a structured rehabilitation method, which includes imitation, repetition, and reinforcement learning, and aims to assess if this method can improve multifunctional prosthetic control. A left below elbow amputee, with 4 years of experience in prosthetic use, took part in this case study. The prosthesis used was a Michelangelo hand with wrist rotation, and the added features of wrist flexion and extension, which allowed more combinations of hand movements. The participant&#8217;s Southampton Hand Assessment Procedure score improved from 58 to 71 following structured training. This suggests that a structured training protocol of imitation, repetition and reinforcement may have a role in learning to control a new prosthetic hand. A larger clinical study is however required to support these findings.

The baseline SHAP performance of the participant with his daily prosthesis was 81 when measured by the clinical staff 8 months prior to testing. A SHAP score of 100 represents able-bodied hand function.24 The participant scored an overall SHAP score of 58 during the na&#239;ve session with the more advanced prosthesis control system. However, 3 months later and with no further interaction with the new system, aside from the structured training, the participant achieved a SHAP score of 71 with the same advanced...

Watch this Scientific Journal Video about A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study at JoVE.com

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

As prosthetic development moves towards the goal of natural control, harnessing amputees&#8217; inherent ability to learn new motor skills may enable proficiency. This manuscript describes a structured rehabilitation protocol, which includes imitation, repetition, and reinforcement learning strategies, for improved multifunctional prosthetic control.

Advances in robotic systems have resulted in prostheses for the upper limb that can produce multifunctional movements. However, these sophisticated ...

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Research

JoVE Journal

Behavior

9.3K Views.  Christian Doppler Laboratory for Restoration of Extremity Function. As prosthetic development moves towards the goal of natural control, harnessing amputees’ inherent ability to learn new motor skills may enable proficiency. This manuscript describes a structured rehabilitation protocol, which includes imitation, repetition, and reinforcement learning strategies, for improved multifunctional prosthetic control.

Video: A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. Higher cognitive functions that require precise control over sensorimotor responses such as decision-making and attentional modulation, however, are typically assessed in nonhuman primates. Despite the richness of primate behavior that allows multiple variants of these functions to be studied, the rodent model remains an attractive, cost-effective alternative to primate models. Furthermore, the ability to fully automate operant conditioning in rodents adds unique advantages over the labor intensive training of nonhuman primates while studying a broad range of these complex functions.
Here, we introduce a protocol for operantly conditioning rats on performing working memory tasks. During critical epochs of the task, the protocol ensures that the animal's overt movement is minimized by requiring the animal to 'fixate' until a Go cue is delivered, akin to nonhuman primate experimental design. A simple two alternative forced choice task is implemented to demonstrate the performance. We discuss the application of this paradigm to other tasks.

A fully automated protocol for rodent operant conditioning is proposed. The protocol relies on precise temporal control of behavioral events to investigate the extent to which this control influences neural activity underlying sensorimotor integration and cognitive control experiments.

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. ...

The 5-Choice Serial Reaction Time Task: A Task of Attention and Impulse Control for Rodents

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. Test day challenges, modifications to the standard task, can be used to systematically tax the neural systems controlling either attention or impulse control. Importantly, these challenges have consistent effects on behavior across laboratories in intact animals and can reveal either enhancements or deficits in cognitive function that are not apparent when rats are only tested on the standard task. The variety of behavioral measures that are collected can be used to determine if other factors (i.e., sedation, motivation deficits, locomotor impairments) are contributing to changes in performance. The versatility of the 5CSRTT is further enhanced because it is amenable to combination with pharmacological, molecular, and genetic techniques.

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. Test day challenges, which are modifications of the standard task, increase flexibility of the task and can be combined with other manipulations to more fully characterize behavior.

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. ...

A Protocol for Housing Mice in an Enriched Environment

Environmental enrichment (EE) is a housing environment for mice that boosts mental and physical health compared to standard laboratory housing. Our recent studies demonstrate that environmental enrichment decreases adiposity, increases energy expenditure, resists diet induced obesity, and causes cancer remission and inhibition in mice. EE typically consists of larger living space, a variety of &#8216;toys&#8217; to interact with, running wheels, and can include a number of other novel environmental changes. All of this fosters a more complex social engagement, cognitive and physical stimulations. Importantly, the toy location and type of toy is changed regularly, which encourages the mice to adapt to a frequently changing and novel environment. Many variables can be manipulated in EE to promote health effects in mice. Thus these approaches are difficult to control and must be properly managed to successfully replicate the associated phenotypes. Therefore, the goal of this video is to demonstrate how EE is properly set up and maintained to assure a complex, challenging, and controlled environment so that other researchers can easily reproduce the protective effects of EE against obesity and cancer.

Environmental enrichment for mice requires a complex and challenging setup, as well as comprehensive husbandry and handling techniques to assure robust metabolic and anti-cancer effects in the mice. This protocol provides detailed procedures to reproduce the above mentioned effects in mice.

Environmental enrichment (EE) is a housing environment for mice that boosts mental and physical health compared to standard laboratory housing. Our recent ...

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

The study of neuromuscular control of movement in humans is accomplished with numerous technologies. Non-invasive methods for investigating neuromuscular function include transcranial magnetic stimulation, electromyography, and three-dimensional motion capture. The advent of readily available and cost-effective virtual reality solutions has expanded the capabilities of researchers in recreating &#8220;real-world&#8221; environments and movements in a laboratory setting. Naturalistic movement analysis will not only garner a greater understanding of motor control in healthy individuals, but also permit the design of experiments and rehabilitation strategies that target specific motor impairments (e.g. stroke). The combined use of these tools will lead to increasingly deeper understanding of neural mechanisms of motor control. A key requirement when combining these data acquisition systems is fine temporal correspondence between the various data streams. This protocol describes a multifunctional system&#8217;s overall connectivity, intersystem signaling, and the temporal synchronization of recorded data. Synchronization of the component systems is primarily accomplished through the use of a customizable circuit, readily made with off the shelf components and minimal electronics assembly skills.

Transcranial magnetic stimulation, electromyography, and 3D motion capture are commonly used non-invasive techniques for investigating neuromuscular function in humans. In this paper, we describe a protocol that synchronously samples data generated by all three of these tools along with the unique addition of virtual reality stimulus presentation and feedback.

The study of neuromuscular control of movement in humans is accomplished with numerous technologies. Non-invasive methods for investigating neuromuscular ...

Event-related Potentials During Target-response Tasks to Study Cognitive Processes of Upper Limb Use in Children with Unilateral Cerebral Palsy

Unilateral Cerebral Palsy (CP) is a neurodevelopmental disorder that is a very common cause of disability in childhood. It is characterized by unilateral motor impairments that are frequently dominated in the upper limb. In addition to a reduced movement capacity of the affected upper limb, several children with unilateral CP show a reduced awareness of the remaining movement capacity of that limb. This phenomenon of disregarding the preserved capacity of the affected upper limb is regularly referred to as Developmental Disregard (DD). Different theories have been postulated to explain DD, each suggesting slightly different guidelines for therapy. Still, cognitive processes that might additionally contribute to DD in children with unilateral CP have never been directly studied. The current protocol was developed to study cognitive aspects involved in upper limb control in children with unilateral CP with and without DD. This was done by recording event-related potentials (ERPs) extracted from the ongoing EEG during target-response tasks asking for a hand-movement response. ERPs consist of several components, each of them associated with a well-defined cognitive process (e.g., the N1 with early attention processes, the N2 with cognitive control and the P3 with cognitive load and mental effort). Due to its excellent temporal resolution, the ERP technique enables to study several covert cognitive processes preceding overt motor responses and thus allows insight into the cognitive processes that might contribute to the phenomenon of DD. Using this protocol adds a new level of explanation to existing behavioral studies and opens new avenues to the broader implementation of research on cognitive aspects of developmental movement restrictions in children.

Several children with unilateral Cerebral Palsy seem to disregard the preserved capacity of their affected upper limb. This Developmental Disregard is extensively described in the literature but the involved cognitive processes have not been studied. To study underlying cognitive factors of upper limb control, an event-related potential protocol was developed.

Unilateral Cerebral Palsy (CP) is a neurodevelopmental disorder that is a very common cause of disability in childhood. It is characterized by unilateral ...

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis (AFEA) software. Additionally, we demonstrate a method for extracting relevant emotional data output and plotting the emotional response of a population over a specified time frame. By time pairing each participant's treatment response to a control stimulus (baseline), the overall emotional response over time and across multiple participants can be quantified. AFEA is a prospective analytical tool for assessing unbiased response to food and beverages. At present, most research has mainly focused on beverages. Methodologies and analyses have not yet been standardized for the application of AFEA to beverages and foods; however, a consistent standard methodology is needed. Optimizing video capture procedures and resulting video quality aids in a successful collection of emotional response to foods. Furthermore, the methodology of data analysis is novel for extracting the pertinent data relevant to the emotional response. The combinations of video capture optimization and data analysis will aid in standardizing the protocol for automated facial expression analysis and interpretation of emotional response data.

A protocol for capturing and statistically analyzing emotional response of a population to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis software is described.

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial ...

Tickling, a Technique for Inducing Positive Affect When Handling Rats

Handling small animals such as rats can lead to several adverse effects. These include the fear of humans, resistance to handling, increased injury risk for both the animals and the hands of their handlers, decreased animal welfare, and less valid research data. To minimize negative effects on experimental results and human-animal relationships, research animals are often habituated to being handled. However, the methods of habituation are highly variable and often of limited effectiveness. More potently, it is possible for humans to mimic aspects of the animals&#39; playful rough-and-tumble behavior during handling. When applied to laboratory rats in a systematic manner, this playful handling, referred to as tickling, consistently gives rise to positive behavioral responses. This article provides a detailed description of a standardized rat tickling technique. This method can contribute to future investigations into positive affective states in animals, make it easier to handle rats for common husbandry activities such as cage changing or medical/research procedures such as injection, and be implemented as a source of social enrichment. It is concluded that this method can be used to efficiently and practicably reduce rats&#39; fearfulness of humans and improve their welfare, as well as reliably model positive affective states.

This article demonstrates the standardized application of playful handling, a tickling technique designed to mimic rat rough-and-tumble play. This technique is effective at reducing fearful reactions to humans and generating positive affect when rats are handled for common husbandry activities and medical and research procedures such as injection.

Handling small animals such as rats can lead to several adverse effects. These include the fear of humans, resistance to handling, increased injury risk ...

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

We have recently demonstrated that implanting intracortical microelectrodes in the motor corteces of rats&#160;results in immediate and lasting motor deficits. Motor impairments were manually quantified through an open field grid test to measure the gross motor function and through a ladder test to measure the fine motor function. Here, we discuss a technique for the automated quantification of the video-recorded tests using our custom Capadona Behavioral Video Analysis System: Grid and Ladder Test, or BVAS. Leveraging simple and readily available coding software (see the Table of Materials), this program allows for the tracking of a single animal on both the open field grid and the ladder tests. In open field grid tracking, the code thresholds the video for intensity, tracks the position of the rat over the 3 min duration of the grid test, and analyzes the path. It then computes and returns measurements for the total distance traveled, the maximum velocity achieved, the number of left- and right-handed turns, and the total number of grid lines crossed by the rat. In ladder tracking, the code again thresholds the video for intensity, tracks the movement of the rat across the ladder, and returns calculated measurements including the time it took the rat to cross the ladder, the number of paw slips occurring below the plane of the ladder rungs, and the incidence of failures due to stagnation or reversals. We envision that the BVAS developed here can be employed for the analysis of motor function in a variety of applications, including many injury or disease models.

The current study aimed to automate the quantification of motor deficits in rats. The initial evaluation model assesses motor loss resulting from an intracortical microelectrode implantation in the motor cortex. We report on the development and use of a tracking algorithm using easily adaptable, simple, and readily available coding software.

We have recently demonstrated that implanting intracortical microelectrodes in the motor corteces of rats&#160;results in immediate and lasting motor ...

A Wind Tunnel for Odor Mediated Insect Behavioural Assays

Olfaction is the most important sensory mechanism by which many insects interact with their environment and a wind tunnel is an excellent tool to study insect chemical ecology. Insects can locate point sources in a three-dimensional environment through the sensory interaction and sophisticated behavior. The quantification of this behavior is a key element in the development of new tools for pest control and decision support. A wind tunnel with a suitable flight section with laminar air flow, visual cues for in-flight feedback and a variety of options for the application of odors can be used to measure complex behaviour which subsequently may allow the identification of attractive or repellent odors, insect flight characteristics, visual-odor interactions and interactions between attractants and odors lingering as background odors in the environment. A wind tunnel holds the advantage of studying the odor mediated behavioural repertoire of an insect in a laboratory setting. Behavioural measures in a controlled setting provide the link between the insect physiology and field application. A wind tunnel must be a flexible tool and should easily support the changes to setup and hardware to fit different research questions. The major disadvantage to the wind tunnel setup described here, is the clean odor background which necessitates special attention when developing a synthetic volatile blend for field application.

Here, we describe the construction and use of a wind tunnel for odor mediated behavioural assays with insects. The wind tunnel design facilitates the release of odor sources by several methods, with and without visual stimuli. Wind tunnel experiments are important methods to identify behaviorally active volatile chemicals.

Olfaction is the most important sensory mechanism by which many insects interact with their environment and a wind tunnel is an excellent tool to study ...

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies

Over the last decade, employing computer animations for animal behavior research has increased due to its ability to non-invasively manipulate the appearance and behavior of visual stimuli, compared to manipulating live animals. Here, we present the FishSim Animation Toolchain, a software framework developed to provide researchers with an easy-to-use method for implementing 3D computer animations in behavioral experiments with fish. The toolchain offers templates to create virtual 3D stimuli of five different fish species. Stimuli are customizable in both appearance and size, based on photographs taken of live fish. Multiple stimuli can be animated by recording swimming paths in a virtual environment using a video game controller. To increase standardization of the simulated behavior, the prerecorded swimming path may be replayed with different stimuli. Multiple animations can later be organized into playlists and presented on monitors during experiments with live fish.
In a case study with sailfin mollies (Poecilia latipinna), we provide a protocol on how to conduct a mate-choice copying experiment with FishSim. We utilized this method to create and animate virtual males and virtual model females, and then presented these to live focal females in a binary choice experiment. Our results demonstrate that computer animation may be used to simulate virtual fish in a mate-choice copying experiment to investigate the role of female gravid spots as an indication of quality for a model female in mate-choice copying.
Applying this method is not limited to mate-choice copying experiments but can be used in various experimental designs. Still, its usability depends on the visual capabilities of the study species and first needs validation. Overall, computer animations offer a high degree of control and standardization in experiments and bear the potential to &#39;reduce&#39; and &#39;replace&#39; live stimulus animals as well as to &#39;refine&#39; experimental procedures.

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies

Using the novel FishSim Animation Toolchain, we present a protocol for non-invasive visual manipulation of public information in the context of mate-choice copying in sailfin mollies. FishSim Animation Toolchain provides an easy-to-use framework for the design, animation and presentation of computer-animated fish stimuli for behavioral experiments with live test fish.