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Transhumeral amputees experience considerable difficulties with controlling a multifunctional prosthesis (powered hand, wrist, and elbow) due to the lack of available muscles to provide electromyographic (EMG) signals. The residual limb motion strategy has become a popular alternative for transhumeral prosthesis control. It provides an intuitive way to estimate the motion of the prosthesis based on the residual shoulder motion, especially for target reaching tasks. Conventionally, a predictive model, typically an artificial neural network (ANN), is directly trained and relied upon to map the shoulder–elbow kinematics using the data from able-bodied subjects without extracting any prior synergistic information. However, it is essential to explicitly identify effective synergies and make them transferable across amputee users for higher accuracy and robustness. To overcome this limitation of the conventional ANN learning approach, this study explicitly combines the kinematic synergies with a recurrent neural network (RNN) to propose a synergy-space neural network for estimating forearm motions (i.e., elbow joint flexion–extension and pronation–supination angles) based on residual shoulder motions. We tested 36 training strategies for each of the 14 subjects, comparing the proposed synergy-space and conventional neural network learning approaches, and we statistically evaluated the results using Pearson’s correlation method and the analysis of variance (ANOVA) test. The offline cross-subject analysis indicates that the synergy-space neural network exhibits superior robustness to inter-individual variability, demonstrating the potential of this approach as a transferable and generalized control strategy for transhumeral prosthesis control.

A key challenge associated with myoelectric prosthesis limbs is the acquisition of a good quality gesture intent signal from the residual anatomy of an amputee. In this study, the authors aim to overcome this limitation by observing the classification accuracy of the fusion of wearable electromyography (EMG) and near-infrared (NIR) to classify eight hand gesture motions across 12 able-bodied participants. As part of the study, they investigate the classification accuracy across a multi-layer perceptron neural network, linear discriminant analysis and quadratic discriminant analysis for different sensing configurations, i.e. EMG-only, NIR-only and EMG-NIR. A separate offline ultrasound scan was conducted as part of the study and served as a ground truth and contrastive basis for the results picked up from the wearable sensors, and allowed for a closer study of the anatomy along the humerus during gesture motion. Results and findings from the work suggest that it could be possible to further develop transhumeral prosthesis using affordable, ergonomic and wearable EMG and NIR sensing, without the need for invasive neuromuscular sensors or further hardware complexity.

Most prosthetic limbs can autonomously move with dexterity, yet they are not perceived by the user as belonging to their own body. Robotic limbs can convey information about the environment with higher precision than biological limbs, but their actual performance is substantially limited by current technologies for the interfacing of the robotic devices with the body and for transferring motor and sensory information bidirectionally between the prosthesis and the user. In this Perspective, we argue that direct skeletal attachment of bionic devices via osseointegration, the amplification of neural signals by targeted muscle innervation, improved prosthesis control via implanted muscle sensors and advanced algorithms, and the provision of sensory feedback by means of electrodes implanted in peripheral nerves, should all be leveraged towards the creation of a new generation of high-performance bionic limbs. These technologies have been clinically tested in humans, and alongside mechanical redesigns and adequate rehabilitation training should facilitate the wider clinical use of bionic limbs. This Perspective argues that technologies for the neural interfacing of robotic devices with the body that have been clinically tested in humans should be leveraged toward the creation of a new generation of high-performance bionic limbs.

Infection is a known complication of the placement of cardiac implantable electronic devices. In this randomized, controlled trial involving 6983 patients undergoing cardiac-device placement, an antibacterial envelope was studied to determine infection prevention. The envelope reduced infection by 40%.

Measurement of muscle contraction is mainly achieved through electromyography (EMG) and is an area of interest for many biomedical applications, including prosthesis control and human machine interface. However, EMG has some drawbacks, and there are also alternative methods for measuring muscle activity, such as by monitoring the mechanical variations that occur during contraction. In this study, a new, simple, non-invasive sensor based on a force-sensitive resistor (FSR) which is able to measure muscle contraction is presented. The sensor, applied on the skin through a rigid dome, senses the mechanical force exerted by the underlying contracting muscles. Although FSR creep causes output drift, it was found that appropriate FSR conditioning reduces the drift by fixing the voltage across the FSR and provides voltage output proportional to force. In addition to the larger contraction signal, the sensor was able to detect the mechanomyogram (MMG), i.e., the little vibrations which occur during muscle contraction. The frequency response of the FSR sensor was found to be large enough to correctly measure the MMG. Simultaneous recordings from flexor carpi ulnaris showed a high correlation (Pearson’s r > 0.9) between the FSR output and the EMG linear envelope. Preliminary validation tests on healthy subjects showed the ability of the FSR sensor, used instead of the EMG, to proportionally control a hand prosthesis, achieving comparable performances.

Numerous clinical and research applications necessitate the ability to interface with peripheral nerve fibers to read and control relevant neural pathways. Visceral organ modulation and rehabilitative prosthesis are two areas which could benefit greatly from improved neural interfacing approaches. Therapeutic neural interfacing, or 'bioelectronic medicine', has potential to affect a broad range of disorders given that all the major organs of the viscera are neurally innervated. However, a better understanding of the neural pathways that underlie function and a means to precisely interface with these fibers are required. Existing peripheral nerve interfaces, consisting primarily of electrode-based designs, are unsuited for highly specific (individual axon) communication and/or are invasive to the tissue. Our laboratory has explored an optogenetic approach by which optically sensitive reporters and actuators are targeted to specific cell (axon) types. The nature of such an approach is laid out in this short perspective, along with associated technologies and challenges.

Understanding how upper-limb prostheses are used in daily life helps to improve the design and robustness of prosthesis control algorithms and prosthetic components. However, only a very small fraction of published research includes prosthesis use in community settings. The cost, limited battery life, and poor generalisation may be the main reasons limiting the implementation of home-based applications. In this work, we introduce the design of a cost-effective Arduino-based myoelectric control system with wearable electromyogram (EMG) sensors. The design considerations focused on home studies, so the robustness, user-friendly control adjustments, and user supports were the main concerns. Three control algorithms, namely, direct control, abstract control, and linear discriminant analysis (LDA) classification, were implemented in the system. In this paper, we will share our design principles and report the robustness of the system in continuous operation in the laboratory. In addition, we will show a first real-time implementation of the abstract decoder for prosthesis control with an able-bodied participant.

Background A lack of evidence of compelling clinical benefits is a key factor limiting the adoption of commercialized powered robotic knee prostheses into mainstream clinical practice. Previous studies have demonstrated mixed results, potentially due to a combination of limitations in prosthetic hardware, control algorithms, and testing methodologies. Methods We investigated the clinical effects of a commercialized robotic knee prosthesis (the latest generation Össur Power Knee TM ) with n=7 above-knee amputee participants. Participants with both higher (K4) and lower mobility (K3) completed a series of experiments including repeated sitting and standing, a stand, walk, sit shuttle test, and fast walking on a treadmill. We tested both standard (ÖSSR) and novel (HKIC) control policies and compared the resulting clinical metrics to those found with the users’ prescribed passive prostheses. Our experiments were physically demanding, which could help elucidate the potential benefits of powered knees. Results The clinical effects of the Power Knee varied with mobility level and the control policy used. The phase-based controller often produced stronger walking and sit/stand improvements for the higher mobility group compared to the default controller, though it also presented a steeper learning curve and reduced walk-to-sit transition speed. Conversely, the default control policy was perceived as easier to master but was less assistive to the higher mobility group and produced slower sit/stand cycles. Lower mobility participants experienced improvements in standing speed (HKIC: % faster, ; ÖSSR: % faster, ), inter-limb ground reaction force symmetry (HKIC: , ; ÖSSR: , ), and inter-limb peak knee moment symmetry (HKIC: , ; ÖSSR: , ) during sit-to-stand tasks relative to their passive prostheses. In contrast, higher mobility participants benefited less in sit/stand but showed improvements while walking including increased toe clearance (HKIC: mm, ; ÖSSR: mm, ), greater early stance knee flexion (HKIC: , ; ÖSSR: , ), and, for the HKIC policy, a reduced swing-phase peak hip flexion moment (HKIC: Nm/kg/(m/s), ). Despite these biomechanical improvements and qualitative reports of reduced effort, neither control policy produced significant benefits in endurance or repeated task performance compared to the passive condition. Sit-to-stand cycle count in the lower mobility group was unchanged (HKIC: , ÖSSR: ), and it was reduced in the higher mobility group with the ÖSSR condition ( fewer, ). In the shuttle walk test, laps completed by higher mobility users decreased with HKIC ( fewer, ), and no significant differences were found for lower mobility users. No significant changes in fast walking distance or speed were observed across conditions. Conclusions The latest generation Power Knee can create clinical improvements in walking and sit/stand behaviors compared to passive (microprocessor) knees, though the effects are sensitive to the user’s mobility level and the Power Knee’s control policy. However, these improvements did not directly translate to improved functional performance or endurance. Some negative effects of the Power Knee were also observed including reduced agility, slower transitions, and thermal limitations, though some of these limitations could potentially be addressed through future control innovations or with more thorough acclimation. The observed benefits motivate future longitudinal studies to investigate the clinical effects of robotic knees compared to passive (microprocessor) knees in real-world settings and to elucidate how they could be best utilized in clinical practice. Trial Registration : The experimental protocol was approved by the University of Michigan Institutional Review Board (HUM00230065) on February 9th, 2024. The trial is registered with the National Institutes of Health under ClinicalTrials.gov ID NCT06138977.

Background Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. Methods We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. Results The regression approach using neural features outperformed regression on classic global EMG features (average R 2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). Conclusions These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control.

Background Many patients develop recurrent periprosthetic joint infection after two-stage exchange arthroplasty of the hip or knee. One potential but insufficiently tested strategy to decrease the risk of persistent or recurrent infection is to administer additional antibiotics after the second-stage reimplantation. Questions/purposes (1) Does a 3-month course of oral antibiotics decrease the risk of failure secondary to infection after a two-stage exchange? (2) Are there any complications related to the administration of oral antibiotics after a two-stage exchange? (3) In those patients who develop a reinfection, is the infecting organism different from the initial infection? Methods Patients at seven centers randomized to receive 3 months of oral antibiotics or no further antibiotic treatment after operative cultures after the second-stage reimplantation were negative. Adult patients undergoing two-stage hip or knee revision arthroplasty for a periprosthetic infection who met Musculoskeletal Infection Society (MSIS) criteria for infection at the first stage were included. Oral antibiotic therapy was tailored to the original infecting organism(s) in consultation with an infectious disease specialist. MSIS criteria as used by the treating surgeon defined failure. Surveillance of patients for complications, including reinfection, occurred at 3 weeks, 6 weeks, 3 months, 12 months, and 24 months. If an organism demonstrated the same antibiotic sensitivities as the original organism, it was considered the same organism; no DNA subtyping was performed. Analysis was performed as intent to treat with all randomized patients included in the groups to which they were randomized. A log-rank survival curve was used to analyze the primary outcome of reinfection. At planned interim analysis (enrollment is ongoing), 59 patients were successfully randomized to the antibiotic group and 48 patients to the control group. Fifty-seven patients had an infection after TKA and 50 after a THA. There was no minimum followup for inclusion in this analysis. The mean followup was 14 months in the antibiotic group and 10 months in the control group. Results Patients treated with oral antibiotics failed secondary to infection less frequently than those not treated with antibiotics (5% [three of 59] versus 19% [nine of 48]; hazard ratio, 4.37; 95% confidence interval, 1.297–19.748; p = 0.016). Three patients had an adverse reaction to the oral antibiotics severe enough to cause them to stop taking the antibiotics early, and four patients who were randomized to that group did not take the antibiotics as directed. With the numbers available, there were no differences between the study groups in terms of the likelihood that an infection after treatment would be with a new organism (eight of nine in the control group versus one of three in the treatment group, p = 0.087). Conclusions This multicenter randomized trial suggests that at short-term followup, the addition of 3 months of oral antibiotics appeared to improve infection-free survival. As a planned interim analysis, however, these results may change as the study reaches closure and the safety profile may yet prove risky. Further followup of this cohort of patients will be necessary to determine whether these preliminary results are durable over time. Level of Evidence Level I, therapeutic study.