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Advanced sensory feedback from upper limb prostheses would provide multiple benefits to people with upper limb amputations, but achieving functional and natural-feeling sensation is technologically challenging. Advances are being made with invasive and non-invasive stimulation approaches, but considerable challenges need to be addressed with technological innovation.

Human hands can perform far more gestures than the number of muscles controlling them, as most gestures result from coordinated combinations of muscle activations and relaxations. This complexity poses a key challenge for human-machine interfaces performing gesture classification based on electromyography (EMG). Rather than identifying all conceivable gestures, it may be simpler to instead identify the activity of the individual muscles which generate a variety of complicated gestures. Here we suggest a three-dimensional model with volume representations of individual digit extensor muscles, averaged across multiple individuals, and evaluate its application and performance in hand gesture classification. Time-domain peaks in high-density surface EMG data from different hand gestures were extracted and localized within the model, from which a gesture classification scheme was generated for both single and multi-label cases. The model was created and tested on a publicly available dataset with 19 participants, leveraging a leave-one-out approach to assess inter-subject generalizability, and multi-label data to assess generalizability to gestures not included in the creation of the model. For single-label classification performance, true positive rates were between 61.9 and 95.1%, with false positive rates between 0 and 24.1%, for different single-digit extensions. The multi-label test demonstrated some degree of generalizability in identifying completely new gesture compositions, while simultaneously maintaining the leave-one-out approach for inter-subject generalizability. A model generated with this approach could be used for gesture classification by anyone, without individual modelling data, with the potential to generalize to any number of gesture compositions.

The spatial distribution of muscle fibre activity is of interest in guiding therapy and assessing recovery of motor function following injuries of the peripheral or central nervous system. This paper presents a new method for stable estimation of motor unit territory centres from high-density surface electromyography (HDsEMG). This completely automatic process applies principal component compression and a rotatable Gaussian surface fit to motor unit action potential (MUAP) distributions to map the spatial distribution of motor unit activity. Each estimated position corresponds to the signal centre of the motor unit territory. Two subjects were used to test the method on forearm muscles, using two different approaches. With the first dataset, motor units were identified by decomposition of intramuscular EMG and the centre position of each motor unit territory was estimated from synchronized HDsEMG data. These positions were compared to the positions of the intramuscular fine wire electrodes with depth measured using ultrasound. With the second dataset, decomposition and motor unit localization was done directly on HDsEMG data, during specific muscle contractions. From the first dataset, the mean estimated depth of the motor unit centres were 8.7, 11.6, and 9.1 mm, with standard deviations 0.5, 0.1, and 1.3 mm, and the respective depths of the fine wire electrodes were 8.4, 15.8, and 9.1 mm. The second dataset generated distinct spatial distributions of motor unit activity which were used to identify the regions of different muscles of the forearm, in a 3-dimensional and projected 2-dimensional view. In conclusion, a method is presented which estimates motor unit centre positions from HDsEMG. The study demonstrates the shifting spatial distribution of muscle fibre activity between different efforts, which could be used to assess individual muscles on a motor unit level.

Measuring human joint dynamics is crucial for understanding how our bodies move and function, providing valuable insights into biomechanics and motor control. Cerebral palsy (CP) is a neurological disorder affecting motor control and posture, leading to diverse gait abnormalities, including altered knee angles. The accurate measurement and analysis of knee angles in individuals with CP are crucial for understanding their gait patterns, assessing treatment outcomes, and guiding interventions. This paper presents a novel multimodal approach that combines inertial measurement unit (IMU) sensors and electromyography (EMG) to measure knee angles in individuals with CP during gait and other daily activities. We discuss the performance of this integrated approach, highlighting the accuracy of IMU sensors in capturing knee joint movements when compared with an optical motion-tracking system and the complementary insights offered by EMG in assessing muscle activation patterns. Moreover, we delve into the technical aspects of the developed device. The presented results show that the angle measurement error falls within the reported values of the state-of-the-art IMU-based knee joint angle measurement devices while enabling a high-quality EMG recording over prolonged periods of time. While the device was designed and developed primarily for measuring knee activity in individuals with CP, its usability extends beyond this specific use-case scenario, making it suitable for applications that involve human joint evaluation.

In contemporary muscle-computer interfaces for upper limb prosthetics there is often a trade-off between control robustness and range of executable movements. As a very low movement error rate is necessary in practical applications, this often results in a quite severe limitation of controllability; a problem growing ever more salient as the mechanical sophistication of multifunctional myoelectric prostheses continues to improve. A possible remedy for this could come from the use of multi-label machine learning methods, where complex movements can be expressed as the superposition of several simpler movements. Here, we investigate this claim by applying a multi-labeled classification scheme in the form of a deep convolutional neural network (CNN) to high density surface electromyography (HD-sEMG) recordings. We use 16 independent labels to model the movements of the hand and forearm state, representing its major degrees of freedom. By training the neural network on 16 × 8 sEMG image sequences 24 samples long with a sampling rate of 2048 Hz to detect these labels, we achieved a mean exact match rate of 78.7% and a mean Hamming loss of 2.9% across 14 healthy test subjects. With this, we demonstrate the feasibility of highly versatile and responsive sEMG control interfaces without loss of accuracy.

Despite the potential of smart home technologies (SHT) to support everyday activities, the implementation rate of such technology in the homes of older adults remains low. The overall aim of this study was to explore factors involved in the decision-making process in adopting SHT among current and future generations of older adults. We also aimed to identify and understand barriers and facilitators that can better support older adults’ engagement in everyday activities. Focus group discussions were used to explore the perspectives of people from diverse age groups (30–39, 50–59, and 70-79-year-olds). Three focus groups met twice at a lab designed as a two-room home equipped with SHT. Our findings revealed that the participants’ decision-making process for adopting SHT involved designs that must be adapted to the changing physical abilities and diverse needs of users. Some conditions, such as ideas for re-invention, were identified after the integration of SHT. Concerns about reliability, complicated interfaces, and value to the user influenced the decision to adopt SHT, highlighting the importance of these factors for successful implementation. Some participants did not fully understand what SHT is nor perceive its benefits, but they expressed a desire to acquire the skills and knowledge to operate SHT. Furthermore, participants desired SHT that can support an active lifestyle. The perceived advantages of SHT include enhancing the sense of security and safety, which can facilitate engagement in everyday activity. Some participants experienced a positive impact on quality of life, related to comfortable living with the implementation of SHT. Adults across age groups perceive that SHT can enhance engagement in everyday activity and the sense of safety and security. However, it is essential to identify solutions for better usability. More collaborative efforts involving diverse stakeholders are vital to bridge the disconnect between SHT design and users’ needs and preferences.

Although seemingly effortless, the control of the human hand is backed by an elaborate neuro-muscular mechanism. The end result is typically a smooth action with the precise positioning of the joints of the hand and an exerted force that can be modulated to enable precise interaction with the surroundings. Unfortunately, even the most sophisticated technology cannot replace such a comprehensive role but can offer only basic hand functionalities. This issue arises from the drawbacks of the prosthetic hand control strategies that commonly rely on surface EMG signals that contain a high level of noise, thus limiting accurate and robust multi-joint movement estimation. The use of intramuscular EMG results in higher quality signals which, in turn, lead to an improvement in prosthetic control performance. Here, we present the evaluation of fourteen common/well-known algorithms (mean absolute value, variance, slope sign change, zero crossing, Willison amplitude, waveform length, signal envelope, total signal energy, Teager energy in the time domain, Teager energy in the frequency domain, modified Teager energy, mean of signal frequencies, median of signal frequencies, and firing rate) for the direct and proportional control of a prosthetic hand. The method involves the estimation of the forces generated in the hand by using different algorithms applied to iEMG signals from our recently published database, and comparing them to the measured forces (ground truth). The results presented in this paper are intended to be used as a baseline performance metric for more advanced algorithms that will be made and tested using the same database.

Hand movement is controlled by a large number of muscles acting on multiple joints in the hand and forearm. In a forearm amputee the control of a hand prosthesis is traditionally depending on electromyography from the remaining forearm muscles. Technical improvements have made it possible to safely and routinely implant electrodes inside the muscles and record high-quality signals from individual muscles. In this study, we present a database of intramuscular EMG signals recorded with fine-wire electrodes alongside recordings of hand forces in an isometric setup and with the addition of spike-sorted metadata. Six forearm muscles were recorded from twelve able-bodied subjects and nine forearm muscles from two subjects. The fully automated recording protocol, based on command cues, comprised a variety of hand movements, including some requiring slowly increasing/decreasing force. The recorded data can be used to develop and test algorithms for control of a prosthetic hand. Assessment of the signals was done in both quantitative and qualitative manners.Measurement(s)muscle electrophysiology trait • muscle contractionTechnology Type(s)micro electrode • strain gaugesFactor Type(s)age • muscleSample Characteristic - OrganismHomo sapiensMachine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11310011

Background Research involving current and future generations of older adults in the design of smart home technology (SHT) is scarce. In this study, current and future generations of older adults, professionals, and researchers together sought to identify user needs and aspirations for and related to SHT. The aim was to co-produce prioritized ideas for SHT solutions to support engagement in everyday activities in later life. We used a research circle (RC) process to elicit perspectives among members from current and future generations of older adults, professionals with expertise in SHT, and health sciences researchers. Over half of the RC members had no previous experience with SHT. RC members met three times at a live-in instrumented home environment equipped with some SHT. Using this data, we conducted a content analysis. Results SHT solutions for stimulating engagement in everyday activities were among the prioritized ideas. Examples included digital reminders to support everyday structure (e.g., meals, medications), interactive games to stimulate cognitive function and promote social interaction, and exercise prompts to maintain physical health. For safety, a digital door control and camera system was suggested to provide reassurance during home visits. Sensor-based technologies were proposed for monitoring and enabling continued autonomy by identifying changes in movement that may signal the need for timely support. The integration of SHT solutions into familiar objects, such as household furniture, was prioritized to minimize disruption to routines. RC members suggested an SHT system, which can coordinate between the closest neighbor groups to outsource support in urgent situations. Conclusions Future and current generations of older adults express needs and aspirations for SHT that integrates into everyday routines as people age. They prioritize SHT solutions that enhance engagement in everyday activities, safety, security, and social interaction.

Functional electrical stimulation (FES) is used for treating foot drop by delivering electrical pulses to the anterior tibialis muscle during the swing phase of gait. This treatment requires that a patient can walk, which is mostly possible in the later phases of rehabilitation. In the early phase of recovery, the therapy conventionally consists of stretching exercises, and less commonly of FES delivered cyclically. Nevertheless, both approaches minimize patient engagement, which is inconsistent with recent findings that the full rehabilitation potential could be achieved by an active psycho-physical engagement of the patient during physical therapy. Following this notion, we proposed smart protocols whereby the patient sits and ankle movements are FES-induced by self-control. In six smart protocols, movements of the paretic ankle were governed by the non-paretic ankle with different control strategies, while in the seventh voluntary movements of the paretic ankle were used for stimulation triggering. One stroke survivor in the acute phase of recovery participated in the study. During the therapy, the patient’s voluntary ankle range of motion increased and reached the value of normal gait after 15 sessions. Statistical analysis did not reveal the differences between the protocols in FES-induced movements.