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1. Characterizing Residual Muscle Properties in Lower Limb Amputees Using High Density EMG Decomposition: A Pilot Study ^*

   https://doi.org/10.1109/EMBC.2018.8513661  

   Fylstra, Bretta L. ; Dai, Chenyun ; Hu, Xiaogang ; Huang, He Helen ( July 2018 , International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC))

   As research is progressing towards EMG control of lower limb prostheses, it is vital to understand the neurophysiology of the residual muscles in the amputated limb, which has been largely ignored. Therefore, the goal of this study was to characterize the activation patterns (muscle recruitment and motor unit discharge patterns) of the residual muscles of lower limb amputees. One transtibial amputee subject was recruited for this pilot study. The participant wore three high-density EMG electrode pads (8x8 grid with 64 channels) on each limb (a total of six pads) – one on the tibialis anterior (TA), medial gastrocnemius (MG), and lateral gastrocnemius (LG), respectively. The participant was asked to follow a ramping procedure plateauing at 50% of maximum voluntary contraction (MVC) for both the TA and Gastrocnemius muscles. The EMG signals were then decomposed offline; the firing rate and spatial activation patterns of the muscle were analyzed. Results showed slower and more variable firing rate in motor units of residual muscles than those of intact side. In addition, the spatial pattern of muscle activation differed between residual and intact muscles. These results indicate that surface EMG signals recorded from residual muscles present modified signal features from intact shank muscles, whichmore »should be considered when implementing myoelectric control schemes.« less
2. Direct continuous electromyographic control of a powered prosthetic ankle for improved postural control after guided physical training: A case study

   https://doi.org/10.1017/wtc.2021.2  

   Fleming, Aaron ; Huang, Stephanie ; Buxton, Elizabeth ; Hodges, Frank ; Huang, He Helen ( January 2021 , Wearable Technologies)

   Abstract Despite the promise of powered lower limb prostheses, existing controllers do not assist many daily activities that require continuous control of prosthetic joints according to human states and environments. The objective of this case study was to investigate the feasibility of direct, continuous electromyographic (dEMG) control of a powered ankle prosthesis, combined with physical therapist-guided training, for improved standing postural control in an individual with transtibial amputation. Specifically, EMG signals of the residual antagonistic muscles (i.e. lateral gastrocnemius and tibialis anterior) were used to proportionally drive pneumatical artificial muscles to move a prosthetic ankle. Clinical-based activities were used in the training and evaluation protocol of the control paradigm. We quantified the EMG signals in the bilateral shank muscles as well as measures of postural control and stability. Compared to the participant’s daily passive prosthesis, the dEMG-controlled ankle, combined with the training, yielded improved clinical balance scores and reduced compensation from intact joints. Cross-correlation coefficient of bilateral center of pressure excursions, a metric for quantifying standing postural control, increased to .83(±.07) when using dEMG ankle control ( passive device: .39(±.29)) . We observed synchronized activation of homologous muscles, rapid improvement in performance on the first day of the training formore »load transfer tasks, and further improvement in performance across training days (p = .006). This case study showed the feasibility of this dEMG control paradigm of a powered prosthetic ankle to assist postural control. This study lays the foundation for future study to extend these results through the inclusion of more participants and activities.« less
3. Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks

   https://doi.org/10.3390/s22239350  

   Murray, Rosemarie ; Mendez, Joel ; Gabert, Lukas ; Fey, Nicholas P. ; Liu, Honghai ; Lenzi, Tommaso ( December 2022 , Sensors)

   Many people struggle with mobility impairments due to lower limb amputations. To participate in society, they need to be able to walk on a wide variety of terrains, such as stairs, ramps, and level ground. Current lower limb powered prostheses require different control strategies for varying ambulation modes, and use data from mechanical sensors within the prosthesis to determine which ambulation mode the user is in. However, it can be challenging to distinguish between ambulation modes. Efforts have been made to improve classification accuracy by adding electromyography information, but this requires a large number of sensors, has a low signal-to-noise ratio, and cannot distinguish between superficial and deep muscle activations. An alternative sensing modality, A-mode ultrasound, can detect and distinguish between changes in superficial and deep muscles. It has also shown promising results in upper limb gesture classification. Despite these advantages, A-mode ultrasound has yet to be employed for lower limb activity classification. Here we show that A- mode ultrasound can classify ambulation mode with comparable, and in some cases, superior accuracy to mechanical sensing. In this study, seven transfemoral amputee subjects walked on an ambulation circuit while wearing A-mode ultrasound transducers, IMU sensors, and their passive prosthesis. The circuitmore »consisted of sitting, standing, level-ground walking, ramp ascent, ramp descent, stair ascent, and stair descent, and a spatial–temporal convolutional network was trained to continuously classify these seven activities. Offline continuous classification with A-mode ultrasound alone was able to achieve an accuracy of 91.8±3.4%, compared with 93.8±3.0%, when using kinematic data alone. Combined kinematic and ultrasound produced 95.8±2.3% accuracy. This suggests that A-mode ultrasound provides additional useful information about the user’s gait beyond what is provided by mechanical sensors, and that it may be able to improve ambulation mode classification. By incorporating these sensors into powered prostheses, users may enjoy higher reliability for their prostheses, and more seamless transitions between ambulation modes.« less
4. Muscle as a tunable material: implications for achieving muscle-like function in robotic prosthetic devices

   https://doi.org/10.1242/jeb.225086  

   Nishikawa, Kiisa ; Huck, Thomas G. ( October 2021 , Journal of Experimental Biology)

   ABSTRACT An ideal prosthesis should perform as well as or better than the missing limb it was designed to replace. Although this ideal is currently unattainable, recent advances in design have significantly improved the function of prosthetic devices. For the lower extremity, both passive prostheses (which provide no added power) and active prostheses (which add propulsive power) aim to emulate the dynamic function of the ankle joint, whose adaptive, time-varying resistance to applied forces is essential for walking and running. Passive prostheses fail to normalize energetics because they lack variable ankle impedance that is actively controlled within each gait cycle. By contrast, robotic prostheses can normalize energetics for some users under some conditions. However, the problem of adaptive and versatile control remains a significant issue. Current prosthesis-control algorithms fail to adapt to changes in gait required for walking on level ground at different speeds or on ramps and stairs. A new paradigm of ‘muscle as a tunable material’ versus ‘muscle as a motor’ offers insights into the adaptability and versatility of biological muscles, which may provide inspiration for prosthesis design and control. In this new paradigm, neural activation tunes muscle stiffness and damping, adapting the response to applied forces rathermore »than instructing the timing and amplitude of muscle force. A mechanistic understanding of muscle function is incomplete and would benefit from collaboration between biologists and engineers. An improved understanding of the adaptability of muscle may yield better models as well as inspiration for developing prostheses that equal or surpass the functional capabilities of biological limbs across a wide range of conditions.« less
5. Concurrent and Continuous Prediction of Finger Kinetics and Kinematics via Motoneuron Activities

   https://doi.org/10.1109/TBME.2022.3232067  

   Roy, Rinku ; Zheng, Yang ; Kamper, Derek G. ; Hu, Xiaogang ( April 2023 , IEEE Transactions on Biomedical Engineering)

   Objective: Robust neural decoding of intended motor output is crucial to enable intuitive control of assistive devices, such as robotic hands, to perform daily tasks. Few existing neural decoders can predict kinetic and kinematic variables simultaneously. The current study developed a continuous neural decoding approach that can concurrently predict fingertip forces and joint angles of multiple fingers. Methods: We obtained motoneuron firing activities by decomposing high-density electromyogram (HD EMG) signals of the extrinsic finger muscles. The identified motoneurons were first grouped and then refined specific to each finger (index or middle) and task (finger force and dynamic movement) combination. The refined motoneuron groups (separate matrix) were then applied directly to new EMG data in real-time involving both finger force and dynamic movement tasks produced by both fingers. EMG-amplitude-based prediction was also performed as a comparison. Results: We found that the newly developed decoding approach outperformed the EMG-amplitude method for both finger force and joint angle estimations with a lower prediction error (Force: 3.47±0.43 vs 6.64±0.69% MVC, Joint Angle: 5.40±0.50° vs 12.8±0.65°) and a higher correlation (Force: 0.75±0.02 vs 0.66±0.05, Joint Angle: 0.94±0.01 vs 0.5±0.05) between the estimated and recorded motor output. The performance was also consistent for both fingers. Conclusion:more »The developed neural decoding algorithm allowed us to accurately and concurrently predict finger forces and joint angles of multiple fingers in real-time. Significance: Our approach can enable intuitive interactions with assistive robotic hands, and allow the performance of dexterous hand skills involving both force control tasks and dynamic movement control tasks.« less