RESUMEN
Surface electromyogram (EMG) signals find diverse applications in movement rehabilitation and human-computer interfacing. For instance, future advanced prostheses, which use artificial intelligence, will require EMG signals recorded from several sites on the forearm. This requirement will entail complex wiring and data handling. We present the design and evaluation of a bespoke EMG sensing system that addresses the above challenges, enables distributed signal processing, and balances local versus global power consumption. Additionally, the proposed EMG system enables the recording and simultaneous analysis of skin-sensor impedance, needed to ensure signal fidelity. We evaluated the proposed sensing system in three experiments, namely, monitoring muscle fatigue, real-time skin-sensor impedance measurement, and control of a myoelectric computer interface. The proposed system offers comparable signal acquisition characteristics to that achieved by a clinically-approved product. It will serve and integrate future myoelectric technology better via enabling distributed machine learning and improving the signal transmission efficiency.
Asunto(s)
Electromiografía , Diseño de Equipo , Procesamiento de Señales Asistido por Computador , Electromiografía/métodos , Electromiografía/instrumentación , Humanos , Algoritmos , Músculo Esquelético/fisiología , Fatiga Muscular/fisiología , Impedancia Eléctrica , Masculino , Aprendizaje Automático , Reproducibilidad de los Resultados , Interfaz Usuario-Computador , Adulto , Sensibilidad y Especificidad , Antebrazo/fisiología , Contracción Muscular/fisiologíaRESUMEN
The limb position effect is a multi-faceted problem, associated with decreased upper-limb prosthesis control acuity following a change in arm position. Factors contributing to this problem can arise from distinct environmental or physiological sources. Despite their differences in origin, the effect of each factor manifests similarly as increased input data variability. This variability can cause incorrect decoding of user intent. Previous research has attempted to address this by better capturing input data variability with data abundance. In this paper, we take an alternative approach and investigate the effect of reducing trial-to-trial variability by improving the consistency of muscle activity through user training. Ten participants underwent 4 days of myoelectric training with either concurrent or delayed feedback in a single arm position. At the end of training participants experienced a zero-feedback retention test in multiple limb positions. In doing so, we tested how well the skill learned in a single limb position generalized to untrained positions. We found that delayed feedback training led to more consistent muscle activity across both the trained and untrained limb positions. Analysis of patterns of activations in the delayed feedback group suggest a structured change in muscle activity occurs across arm positions. Our results demonstrate that myoelectric user-training can lead to the retention of motor skills that bring about more robust decoding across untrained limb positions. This work highlights the importance of reducing motor variability with practice, prior to examining the underlying structure of muscle changes associated with limb position.
Asunto(s)
Miembros Artificiales , Extremidad Superior , Humanos , Electromiografía/métodos , Extremidad Superior/fisiología , Destreza Motora , AprendizajeRESUMEN
The provision of upper limb prosthetic devices through the National Health Services (NHS) within the United Kingdom is driven by national policies. NHS England have recently published a new policy to provide multi-grip myoelectric hands. The policy highlighted that there was limited evidence to support its deployment and it will be reviewed should new information arise. The clear identification of the evidence gap provides an opportunity for the academic research community to conduct studies that will inform future iterations of this and other upper limb prosthetic related policies. This paper presents a summary of findings and recommendations based on two multi-stakeholder workshops held in June 2022 and July 2022, which explored the design requirements for policy-driven research studies. The workshops involved people from a broad range of stakeholder groups: policy, academia, NHS clinical and management, industry, and a person with upper limb absence. The workshop discussions focused on the research questions that NHS England identified in the policy evidence review: (1) Clinical Effectiveness; (2) Cost Effectiveness; (3) Safety; and (4) Patient Subgroups. The recommendations based on stakeholder discussions included the need to gather qualitative and quantitative research evidence, use goal-based outcome measures, and conduct longitudinal studies. Future research studies also need to address the complexities of conducting national and international policy-driven research, such as clinical resource capacity and participant involvement.