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 group12. The analog sensor inputs were first digitized by the ADCs and streamed into the desktop PC for signal processing. The DAC was used for control output to drive the DC motor in prosthesis through a motor controller. Digital I/Os were used to enable/disable the motor controller. The powered prosthesis was tethered to the desktop PC and powered by a 24 V power supply.
The software was programmed in a development environment suitable for virtual instrumentation running on the desktop PC. The development environment was based on virtual instrumentation, which effectively combines both user-defined software and hardware to implement the customized platform. By using the structure of a graphical block diagram, different modular function nodes can be easily and efficiently implemented and updated. In order to demonstrate the function of platform for online control of powered artificial legs, preliminarily designed prosthesis control was implemented on this platform. The control system included a neural controller and an intrinsic controller. The neural controller consisted of our previous designed NMI based on neuromuscular-mechanical fusion, which recognized the user&#8217;s activity mode. The neural controller as a high-level controller was hierarchically connected with the intrinsic control for powered lower limb prosthesis control.
The architecture of control software on the platform is illustrated in Figure 1. The NMI contains two parts: offline training module and online testing module. The offline training module was designed to collect training data and build the classifiers in NMI. The collected multichannel surface EMG signals and mechanical measurements were first preprocessed and segmented into continuous sliding windows. In each window, features which characterize the signal patterns were extracted and then fused into one feature vector. The feature vector in each window was labeled with activity modes (classes) and phase index based on the performing activities of prosthesis user and the states of prosthesis during the training data collection. The labeled feature vectors were then used to build a phase-dependent pattern classifier, which contains multiple sub-classifiers correlated with individually phases. The created classifier was saved and transferred to online testing module for later online evaluation.
The online testing module was used to online recognize user&#8217;s movement intent and switch the activity modes in intrinsic controller. The multichannel neuromuscular and mechanical measurements were simultaneously streamed into online testing module and transformed into feature vectors. Then the feature vectors were fed into the phase-dependent classifier which was already built in offline training module. Based on the current phase in intrinsic controller, the corresponding sub-classifier was switched on and used to recognize the user&#8217;s intent. The classification output was further post-processed and sent to intrinsic controller to switch activity modes.
A finite-state machine (FSM) based impedance controller was implemented for the intrinsic control of powered artificial legs. The impedance controller generated desired torque output on the knee joints. The finite-state machine adjusted the joint impedance according to the current state of the performing activity. For locomotion activities (i.e. level-ground walking and ramp ascent/descent), the FSM consisted of five states corresponding to five gait phases: stance flexion (STF), stance extension (STE), pre-swing (PSW), swing flexion (SWF), and swing extension (SWE); for static standing, the FSM included two phases: weight bearing (WB) and non-weight bearing (NWB). Transitions between the states were triggered by the ground reaction force and knee joint position. The transition between activity modes was controlled by the output from the online testing module. For all three modules discussed above, graphical user interface (GUI) were built, which allowed experimenters in lab to easily adjust control parameters, monitor system performance, and conduct evaluation experiments.
2. Experimental Setup
<ol>
	<li>Surface Electromyography (sEMG) 
	Surface EMG signals from the thigh muscles of the amputee subject&#8217;s residual limb are recorded by a wireless EMG acquisition system. The targeted thigh muscles included the rectus femoris (RF), vastus laeralis (VL), vastus medialis (VM), biceps femoris long (BFL), sartorius (SAR), semitendinosus (SEM), and adductor magnus (ADM). It is noteworthy that precisely targeting the specific muscle is not necessary8 because the used pattern recognition algorithm in NMI searches activation patterns of multiple muscles to recognize the activity mode. As long as the measured neuromuscular control information for hip and knee control is sufficient, cross-talks among EMG recordings have little impact on the NMI performance.
	<ol>
		<li>Ask the subject to perform hip flexion/extension, hip adduction/abduction, and attempt to flex/extend the amputated knee joint when he is in standing position.</li>
		<li>Approximate and determine the locations of electrode placements by muscle palpation and examination of EMG recordings.</li>
		<li>Embed the electrodes in a new designed EMG electrode-socket interface, as shown in Figure 2, for both the subject&#8217;s comfort and reliable electrode-skin contact.&#160;</li>
	</ol>
	</li>
	<li>Mechanical Measurements from Powered Prosthesis 
	The mechanical ground reaction forces and moments measured by a 6-DOF load cell from the prosthetic pylon are fused with EMG signals to recognize the subject&#8217;s movement intent. In addition, a potentiometer is instrumented on the knee joint to measure the knee joint angle and an encoder was connected to the DC motor to calculate knee angular velocity. These measurements are used as the feedback signals for intrinsic control.
	<ol>
		<li>Mount a six-degree-freedom load cell on the prosthetic pylon.</li>
		<li>Align X-axis, Y-axis, and Z-axis of the load cell with the mediolateral direction, anteroposterior direction, and superoinferior direction of the prosthesis, respectively.&#160;</li>
	</ol>
	</li>
	<li>Laboratory Environment Setup 
	In order to evaluate the neural control of powered artificial legs on transfemoral amputees, an obstacle course was built in the laboratory, as shown in Figure 3. The testing environment included a 5-m straight walkway, a 4-m long ramp with an inclination angle of 8&#176;, and a level platform with the ramp securely attached. Along the ramp, hand railings were installed to enhance the safety of the subject. In addition, a ceiling rail system with a fall-arrest harness was provided to protect the subject from falling during the experiment.</li>
</ol>
3. Experimental Protocol
This study was conducted with the approval of Institutional Review Board (IRB) at the University of Rhode Island and with informed consent of the recruited subject. One male unilateral transfemoral amputee (cause of amputation: trauma; age: 57 years; duration of amputation: 32 years) was recruited in this study. The ratio between the length of the residual limb (measured from the ischial tuberosity to the distal end of the residual limb) to the length of the non-impaired side (measured from the ischial tuberosity to the femoral epicondyle) was 51%. The subject wears a microprocessor-controlled prosthetic knee through a suction suspension socket in his daily life. Prior to the experiment in this study, this subject received several training sessions led by a physical therapist in order to let the subject to adapt to the powered device and calibrate the desired impedance in each activity mode.
<ol>
	<li>Subject Preparation
	<ol>
		<li>Measure the subject&#8217;s weight, height, and record his gender and age.</li>
		<li>Ask the subject to put on his own shorts in a private preparation room.</li>
		<li>Put a size-fitted fall-arrest harness on the subject and attach it to the ceiling rail system.&#160;</li>
	</ol>
	</li>
	<li>Preparation for EMG Recording
	<ol>
		<li>Select seven fully charged wireless EMG sensors and turn them on.</li>
		<li>Place the EMG sensors into the customized suction socket at prepared locations. Write down the order number of the sensors and associate them with EMG locations.</li>
		<li>Clean the skin of subject&#8217;s residual limb with isopropyl alcohol pads.</li>
		<li>Assist the subject in donning the suction socket and verify that the socket is firmly attached to subject&#8217;s residual limb.</li>
		<li>Turn on the real-time EMG analog data streaming software.</li>
		<li>Ask the subject to perform hip flexion/extension, hip adduction/abduction, and knee flexion/extension and examine EMG signals to verify EMG electrode contact and data transmission.</li>
	</ol>
	</li>
	<li>Alignment and Initial Calibration of Powered Prosthetic Leg
	<ol>
		<li>Instruct the subject to remain in a standing position while holding an assistive walker.</li>
		<li>Attach the powered prosthesis to the suction socket with a pyramid adaptor. Adjust a set of rotation screws on the adaptor until the position of the prosthesis is geometrically aligned with the socket. This procedure was performed by a prosthetist.</li>
		<li>Ask the subject to lift the prosthesis off the ground and calibrate the load cell on the prosthetic pylon.</li>
		<li>Instruct the subject to practice walking on different terrains (e.g., level-ground, ramp ascent, and ramp descent) when wearing the powered prosthetic leg. This procedure continues until the subject feels confident in walking with the powered device and yields consistent gait pattern in each activity performance.&#160;</li>
	</ol>
	</li>
	<li>Training Data Collection for Training the Classifiers in NMI
	<ol>
		<li>Instruct the subject to stand on the starting location of a predefined walking path, as shown in Figure 3.</li>
		<li>Turn on the powered prosthesis and load the parameters into the intrinsic controller.</li>
		<li>Run a training data collection computer program and set the intrinsic control to standing mode by clicking the &#8220;Standing&#8221; button on the graphic user interface (GUI).</li>
		<li>Begin data collection by clicking the &#8220;Start Recording&#8221; button on GUI. Instruct the subject to stay in standing position for 5 sec.</li>
		<li>Instruct the subject to walk over level ground at his/her self-selected comfortable walking speed; at the same time, click the &#8220;Walking&#8221; button on the GUI before toe-off of the leading leg of the subject and set the intrinsic control to level-ground walking mode.</li>
		<li>When the subject was approaching to the edge of ramp ascent, click the &#8220;Ramp Ascent&#8221; button on the GUI before the toe-off of the prosthetic leg stepping on the ramp and switch the intrinsic control to ramp ascent mode. For safety, allow the subject to use a hand railing when walking on a ramp.</li>
		<li>When the subject comes to the end of the ramp, click the &#8220;Walking&#8221; button again before the heel strike of prosthetic leg stepping onto the level platform and switch the prosthesis intrinsic control to level-ground walking mode.</li>
		<li>At the end of the walking path, instruct the subject to stop and remain in standing position. At the same time, click the &#8220;Standing&#8221; button before the double stance phase and switch the intrinsic control back to standing mode.</li>
		<li>After around 5 seconds, terminate data collection by clicking &#8220;Stop&#8221; button. Label collected data as &#8220;training data set 1&#8221;.</li>
		<li>Repeat the procedure 3.4.4-3.4.9 when the subject walks in a reverse route back to the starting location; the only difference is switching the intrinsic control to ramp descent mode when the subject walks on the down ramp.</li>
		<li>Repeat 3.4.4-3.4.10 until ten complete training data sets are collected. Examine the signal quality of collected training data set.</li>
		<li>Allow the subject to have a rest period after the data collection session.</li>
		<li>Train the pattern recognition classifiers in NMI via offline training module (Figure 1). Use the collected EMG and mechanical signals, the activity modes (classes) labeled during the training procedure, and detected phases to build the phase-dependent pattern classifiers. Save parameters of the classifiers automatically for later online testing session.</li>
	</ol>
	</li>
	<li>Online Testing of Neural Control of Powered Transfemoral Prosthesis
	<ol>
		<li>Instruct the subject to stand at the starting point of the walking path.</li>
		<li>Turn on the powered prosthesis. Load the trained classifier to online testing module and the parameters to the intrinsic controller.</li>
		<li>Instruct the subject to begin the testing trials in a standing position, then continuously transition to level-ground walking, ramp walking, level-ground walking, and finally stop and finish this trial at the end of the walking path. Instruct the subject to perform each activity at a comfortable pace. Allow rest periods between trials to avoid fatigue.</li>
		<li>During each testing trial, display the activity modes of the prosthesis and knee joint angle readings on a TV monitor. Save all of the measurements and control outputs for later evaluation purpose.</li>
		<li>Repeat steps 3.5.1-3.5.4 until ten complete testing trials are finished.&#160;</li>
	</ol>
	</li>
</ol>

<ol>
	<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>

No conflicts of interest declared.

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.
<p class="...

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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Bioengineering

16.2K 조회수.  North Carolina State University & University of North Carolina at Chapel Hill.  신경 - 기계 인터페이스 (NMI)는 사용자의 운동 모드를 식별하기 위해 개발되었다. 이러한 NMI가 잠재적으로 전원이 공급되는 인공 다리의 신경 제어에 유용하지만 완전히 입증되지 않았다. 쉽게 구현 및 신경 제어 개발 전원 하체 보철을위한 안전하고 낮은 사지 절단 환자에 neurally 제어 인공 다리를 평가하는 실험실 환경 (2) 실험 장치 및 프로토콜에 대한 (1) ?...

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

Environmentally-controlled Microtensile Testing of Mechanically-adaptive Polymer Nanocomposites for ex vivo Characterization

Implantable microdevices are gaining significant attention for several biomedical applications1-4. Such devices have been made from a range of materials, each offering its own advantages and shortcomings5,6. Most prominently, due to the microscale device dimensions, a high modulus is required to facilitate implantation into living tissue. Conversely, the stiffness of the device should match the surrounding tissue to minimize induced local strain7-9. Therefore, we recently developed a new class of bio-inspired materials to meet these requirements by responding to environmental stimuli with a change in mechanical properties10-14. Specifically, our poly(vinyl acetate)-based nanocomposite (PVAc-NC) displays a reduction in stiffness when exposed to water and elevated temperatures (e.g. body temperature). Unfortunately, few methods exist to quantify the stiffness of materials in vivo15, and mechanical testing outside of the physiological environment often requires large samples inappropriate for implantation. Further, stimuli-responsive materials may quickly recover their initial stiffness after explantation. Therefore, we have developed a method by which the mechanical properties of implanted microsamples can be measured ex vivo, with simulated physiological conditions maintained using moisture and temperature control13,16,17. 
To this end, a custom microtensile tester was designed to accommodate microscale samples13,17 with widely-varying Young's moduli (range of 10 MPa to 5 GPa). As our interests are in the application of PVAc-NC as a biologically-adaptable neural probe substrate, a tool capable of mechanical characterization of samples at the microscale was necessary. This tool was adapted to provide humidity and temperature control, which minimized sample drying and cooling17. As a result, the mechanical characteristics of the explanted sample closely reflect those of the sample just prior to explantation.
The overall goal of this method is to quantitatively assess the in vivo mechanical properties, specifically the Young's modulus, of stimuli-responsive, mechanically-adaptive polymer-based materials. This is accomplished by first establishing the environmental conditions that will minimize a change in sample mechanical properties after explantation without contributing to a reduction in stiffness independent of that resulting from implantation. Samples are then prepared for implantation, handling, and testing (Figure 1A). Each sample is implanted into the cerebral cortex of rats, which is represented here as an explanted rat brain, for a specified duration (Figure 1B). At this point, the sample is explanted and immediately loaded into the microtensile tester, and then subjected to tensile testing (Figure 1C). Subsequent data analysis provides insight into the mechanical behavior of these innovative materials in the environment of the cerebral cortex.

Environmentally-controlled Microtensile Testing of Mechanically-adaptive Polymer Nanocomposites for ex vivo Characterization

A method is discussed by which the in vivo mechanical behavior of stimuli-responsive materials is monitored as a function of time. Samples are tested ex vivo using a microtensile tester with environmental controls to simulate the physiological environment. This work further promotes understanding the in vivo behavior of our material.

Implantable microdevices are gaining significant attention for several biomedical applications1-4. Such devices have been made from a range of materials, ...

Design of a Biaxial Mechanical Loading Bioreactor for Tissue Engineering

We designed a loading device that is capable of applying uniaxial or biaxial mechanical strain to a tissue engineered biocomposites fabricated for transplantation. While the device primarily functions as a bioreactor that mimics the native mechanical strains, it is also outfitted with a load cell for providing force feedback or mechanical testing of the constructs. The device subjects engineered cartilage constructs to biaxial mechanical loading with great precision of loading dose (amplitude and frequency) and is compact enough to fit inside a standard tissue culture incubator. It loads samples directly in a tissue culture plate, and multiple plate sizes are compatible with the system. The device has been designed using components manufactured for precision-guided laser applications. Bi-axial loading is accomplished by two orthogonal stages. The stages have a 50 mm travel range and are driven independently by stepper motor actuators, controlled by a closed-loop stepper motor driver that features micro-stepping capabilities, enabling step sizes of less than 50 nm. A polysulfone loading platen is coupled to the bi-axial moving platform. Movements of the stages are controlled by Thor-labs Advanced Positioning Technology (APT) software. The stepper motor driver is used with the software to adjust load parameters of frequency and amplitude of both shear and compression independently and simultaneously. Positional feedback is provided by linear optical encoders that have a bidirectional repeatability of 0.1 &mu;m and a resolution of 20 nm, translating to a positional accuracy of less than 3 &mu;m over the full 50 mm of travel. These encoders provide the necessary position feedback to the drive electronics to ensure true nanopositioning capabilities. In order to provide the force feedback to detect contact and evaluate loading responses, a precision miniature load cell is positioned between the loading platen and the moving platform. The load cell has high accuracies of 0.15% to 0.25% full scale.

We designed a novel mechanical loading bioreactor that can apply uniaxial or biaxial mechanical strain to a cartilage biocomposite prior to transplantation into an articular cartilage defect.

We  designed a loading device that is capable of applying uniaxial or biaxial  mechanical strain to a tissue engineered biocomposites fabricated for  ...

A Protocol for Bioinspired Design: A Ground Sampler Based on Sea Urchin Jaws

Bioinspired design is an emerging field that takes inspiration from nature to develop high-performance materials and devices. The sea urchin mouthpiece, known as the Aristotle's lantern, is a compelling source of bioinspiration with an intricate network of musculature and calcareous teeth that can scrape, cut, chew food and bore holes into rocky substrates. We describe the bioinspiration process as including animal observation, specimen characterization, device fabrication and mechanism bioexploration. The last step of bioexploration allows for a deeper understanding of the initial biology. The design architecture of the Aristotle's lantern is analyzed with micro-computed tomography and individual teeth are examined with scanning electron microscopy to identify the microstructure. Bioinspired designs are fabricated with a 3D printer, assembled and tested to determine the most efficient lantern opening and closing mechanism. Teeth from the bioinspired lantern design are bioexplored via finite element analysis to explain from a mechanical perspective why keeled tooth structures evolved in the modern sea urchins we observed. This circular approach allows for new conclusions to be drawn from biology and nature.

A protocol for bioinspired design is described for a sampling device based on the jaws of a sea urchin. The bioinspiration process includes observing the sea urchins, characterizing the mouthpiece, 3D printing of the teeth and their assembly, and bioexploring the tooth structure.

Bioinspired design is an emerging field that takes inspiration from nature to develop high-performance materials and devices. The sea urchin mouthpiece, ...

A Microfluidic Platform for High-throughput Single-cell Isolation and Culture

Studying the heterogeneity of single cells is crucial for many biological questions, but is technically difficult. Thus, there is a need for a simple, yet high-throughput, method to perform single-cell culture experiments. Here, we report a microfluidic chip-based strategy for high-efficiency single-cell isolation (~77%) and demonstrate its capability of performing long-term single-cell culture (up to 7 d) and cellular heterogeneity analysis using clonogenic assay. These applications were demonstrated with KT98 mouse neural stem cells, and A549 and MDA-MB-435 human cancer cells. High single-cell isolation efficiency and long-term culture capability are achieved by using different sizes of microwells on the top and bottom of the microfluidic channel. The small microwell array is designed for precisely isolating single-cells, and the large microwell array is used for single-cell clonal culture in the microfluidic chip. This microfluidic platform constitutes an attractive approach for single-cell culture applications, due to its flexibility of adjustable cell culture spaces for different culture strategies, without decreasing isolation efficiency.

Here, we present a protocol for isolating and culturing single cells with a microfluidic platform, which utilizes a new microwell design concept to allow for high-efficiency single cell isolation and long-term clonal culture.

Studying the heterogeneity of single cells is crucial for many biological questions, but is technically difficult. Thus, there is a need for a simple, yet ...

An Experimental and Finite Element Protocol to Investigate the Transport of Neutral and Charged Solutes across Articular Cartilage

Osteoarthritis (OA) is a debilitating disease that is associated with degeneration of articular cartilage and subchondral bone. Degeneration of articular cartilage impairs its load-bearing function substantially as it experiences tremendous chemical degradation, i.e. proteoglycan loss and collagen fibril disruption. One promising way to investigate chemical damage mechanisms during OA is to expose the cartilage specimens to an external solute and monitor the diffusion of the molecules. The degree of cartilage damage (i.e. concentration and configuration of essential macromolecules) is associated with collisional energy loss of external solutes while moving across articular cartilage creates different diffusion characteristics compared to healthy cartilage. In this study, we introduce a protocol, which consists of several steps and is based on previously developed experimental micro-Computed Tomography (micro-CT) and finite element modeling. The transport of charged and uncharged iodinated molecules is first recorded using micro-CT, which is followed by applying biphasic-solute and multiphasic finite element models to obtain diffusion coefficients and fixed charge densities across cartilage zones.

We propose a protocol to investigate the transport of charged and uncharged molecules across articular cartilage with the aid of recently developed experimental and numerical methods.

Osteoarthritis (OA) is a debilitating disease that is associated with degeneration of articular cartilage and subchondral bone. Degeneration of articular ...

A Protocol for Decellularizing Mouse Cochleae for Inner Ear Tissue Engineering

In mammals, mechanosensory hair cells that facilitate hearing lack the ability to regenerate, which has limited treatments for hearing loss. Current regenerative medicine strategies have focused on transplanting stem cells or genetic manipulation of surrounding support cells in the inner ear to encourage replacement of damaged stem cells to correct hearing loss. Yet, the extracellular matrix (ECM) may play a vital role in inducing and maintaining function of hair cells, and has not been well investigated. Using the cochlear ECM as a scaffold to grow adult stem cells may provide unique insights into how the composition and architecture of the extracellular environment aids cells in sustaining hearing function. Here we present a method for isolating and decellularizing cochleae from mice to use as scaffolds accepting perfused adult stem cells. In the current protocol, cochleae are isolated from euthanized mice, decellularized, and decalcified. Afterward, human Wharton&#39;s jelly cells (hWJCs) that were isolated from the umbilical cord were carefully perfused into each cochlea. The cochleae were used as bioreactors, and cells were cultured for 30 days before undergoing processing for analysis. Decellularized cochleae retained identifiable extracellular structures, but did not reveal the presence of cells or noticeable fragments of DNA. Cells perfused into the cochlea invaded most of the interior and exterior of the cochlea and grew without incident over a duration of 30 days. Thus, the current method can be used to study how cochlear ECM affects cell development and behavior.

The goal of this protocol is to demonstrate an effective method to decellularize and decalcify mouse cochleae for utilization as scaffolds for tissue engineering applications.

In mammals, mechanosensory hair cells that facilitate hearing lack the ability to regenerate, which has limited treatments for hearing loss. Current ...

A Novel Surgical Technique As a Foundation for In Vivo Partial Liver Engineering in Rat

Organ engineering is a novel strategy to generate liver organ substitutes that can potentially be used in transplantation. Recently, in vivo liver engineering, including in vivo organ decellularization followed by repopulation, has emerged as a promising approach over ex vivo liver engineering. However, postoperative survival was not achieved. The aim of this study is to develop a novel surgical technique of in vivo selective liver lobe perfusion in rats as a prerequisite for in vivo liver engineering. We generate a circuit bypass only through the left lateral lobe. Then, the left lateral lobe is perfused with heparinized saline. The experiment is performed with 4 groups (n = 3 rats per group) based on different perfusion times of 20 min, 2 h, 3 h, and 4 h. Survival, as well as the macroscopically visible change of color and the histologically determined absence of blood cells in the portal triad and the sinusoids, is taken as an indicator for a successful model establishment. After selective perfusion of the left lateral lobe, we observe that the left lateral lobe, indeed, turned from red to faint yellow. In a histological assessment, no blood cells are visible in the branch of the portal vein, the central vein, and the sinusoids. The left lateral lobe turns red after reopening the blocked vessels. 12/12 rats survived the procedure for more than one week. We are the first to report a surgical model for in vivo single liver lobe perfusion with a long survival period of more than one week. In contrast to the previously published report, the most important advantage of the technique presented here is that perfusion of 70% of the liver is maintained throughout the whole procedure. The establishment of this technique provides a foundation for in vivo partial liver engineering in rats, including decellularization and recellularization.

A Novel Surgical Technique As a Foundation for In Vivo Partial Liver Engineering in Rat

We establish a novel surgical technique for an in vivo single liver lobe perfusion model in rat as a prerequisite for further studying in vivo partial liver engineering in the future.

Organ engineering is a novel strategy to generate liver organ substitutes that can potentially be used in transplantation. Recently, in vivo liver ...

Design and Synthesis of a Reconfigurable DNA Accordion Rack

DNA nanostructure-based mechanical systems or DNA nanomachines, which produce complex nanoscale motion in 2D and 3D in the nanometer to &#229;ngstr&#246;m resolution, show great potential in various fields of nanotechnology such as the molecular reactors, drug delivery, and nanoplasmonic systems. The reconfigurable DNA accordion rack, which can collectively manipulate a 2D or 3D nanoscale network of elements, in multiple stages in response to the DNA inputs, is described. The platform has potential to increase the number of elements that DNA nanomachines can control from a few elements to a network scale with multiple stages of reconfiguration.
In this protocol, we describe the entire experimental process of the reconfigurable DNA accordion rack of 6 by 6 meshes. The protocol includes a design rule and simulation procedure of the structures and a wet-lab experiment for synthesis and reconfiguration. In addition, analysis of the structure using TEM (transmission electron microscopy) and FRET (fluorescence resonance energy transfer) is included in the protocol. The novel design and simulation methods covered in this protocol will assist researchers to use the DNA accordion rack for further applications.

We describe the detailed protocol for design, simulation, wet-lab experiments, and analysis for a reconfigurable DNA accordion rack of 6 by 6 meshes.

DNA nanostructure-based mechanical systems or DNA nanomachines, which produce complex nanoscale motion in 2D and 3D in the nanometer to &#229;ngstr&#246;m ...

A Microfluidic Platform for Stimulating Chondrocytes with Dynamic Compression

Mechanical stimuli are known to modulate biological functions of cells and tissues. Recent studies have suggested that compressive stress alters growth plate cartilage architecture and results in growth modulation of long bones of children. To determine the role of compressive stress in bone growth, we created a microfluidic device actuated by pneumatic pressure, to dynamically (or statically) compress growth plate chondrocytes embedded in alginate hydrogel cylinders. In this article, we describe detailed methods for fabricating and characterizing this device. The advantages of our protocol are: 1) Five different magnitudes of compressive stress can be generated on five technical replicates in a single platform, 2) It is easy to visualize cell morphology via a conventional light microscope, 3) Cells can be rapidly isolated from the device after compression to facilitate downstream assays, and 4) The platform can be applied to study mechanobiology of any cell type that can grow in hydrogels.

This article provides detailed methods for fabricating and characterizing a pneumatically actuating microfluidic device for chondrocyte compression.

Mechanical stimuli are known to modulate biological functions of cells and tissues. Recent studies have suggested that compressive stress alters growth ...

Fabrication and Implementation of a Reference-Free Traction Force Microscopy Platform

Quantifying cell-induced material deformation provides useful information concerning how cells sense and respond to the physical properties of their microenvironment. While many approaches exist for measuring cell-induced material strain, here we provide a methodology for monitoring strain with sub-micron resolution in a reference-free manner. Using a two-photon activated photolithographic patterning process, we demonstrate how to generate mechanically and bio-actively tunable synthetic substrates containing embedded arrays of fluorescent fiducial markers to easily measure three-dimensional (3D) material deformation profiles in response to surface tractions. Using these substrates, cell tension profiles can be mapped using a single 3D image stack of a cell of interest. Our goal with this methodology is to make traction force microscopy a more accessible and easier to implement tool for researchers studying cellular mechanotransduction processes, especially newcomers to the field.

This protocol provides instructions for implementing multiphoton lithography to fabricate three-dimensional arrays of fluorescent fiducial markers embedded in poly(ethylene glycol)-based hydrogels for use as reference-free, traction force microscopy platforms. Using these instructions, measurement of 3D material strain and calculation of cellular tractions is simplified to promote high-throughput traction force measurements.

Quantifying cell-induced material deformation provides useful information concerning how cells sense and respond to the physical properties of their ...