Arduino-Based Myoelectric Control: Towards Longitudinal Study of Prosthesis Use

Highly Flexible and Compressible 3D Interconnected Graphene Foam for Sensitive Pressure Detection

A flexible pressure sensor, capable of effectively detecting forces exerted on soft or deformable surfaces, has demonstrated broad application in diverse fields, including human motion tracking, health monitoring, electronic skin, and artificial intelligence systems. However, the design of convenient sensors with high sensitivity and excellent stability is still a great challenge. Herein, we present a multi-scale 3D graphene pressure sensor composed of two types of 3D graphene foam. The sensor exhibits a high sensitivity of 0.42 kPa-1 within the low-pressure range of 0-390 Pa and 0.012 kPa-1 within the higher-pressure range of 0.4 to 42 kPa, a rapid response time of 62 ms, and exceptional repeatability and stability exceeding 10,000 cycles. These characteristics empower the sensor to realize the sensation of a drop of water, the speed of airflow, and human movements.

Automated H2O2 monitoring during photo-Fenton processes using an Arduino self-assembled automatic system

A cheap and easy to use Arduino self-assembled automatic system was employed to continuously monitor the hydrogen peroxide consumption during the photo-Fenton degradation of caffeine, selected as model target compound. The automatic system made it possible to measure the H2O2 concentration in the reaction cell via a colorimetric reaction and to take samples for HPLC analysis minimising the operator manual intervention and exposure to UV radiation. The obtained results were compared in terms of LOD and LOQ with H2O2 measurements manually performed using UV-Vis spectrophotometry, evidencing better analytical performance when using the automatic system; LOD and LOQ were respectively 0.032 mM and 0.106 mM for the automatic system against 0.064 mM and 0.213 mM for UV-Vis spectrophotometry. Furthermore, the photo-Fenton treatment was optimised by means of a Design of Experiments (DoE) investigating the effect of added H2O2 concentration, iron concentration and caffeine initial concentration on system performances. The use of the automatic device for such monitoring provided several advantages: automation (with consequent reduction of the workload), measurement increased precision, reduced reagents consumption and waste production in agreement with the principles of Green Analytical Chemistry.

DistaNet: grasp-specific distance biofeedback promotes the retention of myoelectric skills

Objective.An active myoelectric interface responds to the user's muscle signals to enable movements. Machine learning can decode user intentions from myoelectric signals. However, machine learning-based interface control lacks continuous, intuitive feedback about task performance, needed to facilitate the acquisition and retention of myoelectric control skills.Approach.We propose DistaNet as a neural network-based framework that extracts smooth, continuous, and low-dimensional signatures of the hand grasps from multi-channel myoelectric signals and provides grasp-specific biofeedback to the users.Main results.Experimental results show its effectiveness in decoding user gestures and providing biofeedback, helping users retain the acquired motor skills.Significance.We demonstrates myoelectric skill retention in a pattern recognition setting for the first time.

On the Applications of EMG Sensors and Signals

The ability to execute limb motions derives from composite command signals (or efferent signals) that stem from the central nervous system through the highway of the spinal cord and peripheral nerves to the muscles that drive the joints [...].

Internet of Things for beyond-the-laboratory prosthetics research

Research on upper-limb prostheses is typically laboratory-based. Evidence indicates that research has not yet led to prostheses that meet user needs. Inefficient communication loops between users, clinicians and manufacturers limit the amount of quantitative and qualitative data that researchers can use in refining their innovations. This paper offers a first demonstration of an alternative paradigm by which remote, beyond-the-laboratory prosthesis research according to user needs is feasible. Specifically, the proposed Internet of Things setting allows remote data collection, real-time visualization and prosthesis reprogramming through Wi-Fi and a commercial cloud portal. Via a dashboard, the user can adjust the configuration of the device and append contextual information to the prosthetic data. We evaluated this demonstrator in real-time experiments with three able-bodied participants. Results promise the potential of contextual data collection and system update through the internet, which may provide real-life data for algorithm training and reduce the complexity of send-home trials. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning

Gesture recognition through surface electromyography (sEMG) provides a new method for the control algorithm of bionic limbs, which is a promising technology in the field of human–computer interaction. However, subject specificity of sEMG along with the offset of the electrode makes it challenging to develop a model that can quickly adapt to new subjects. In view of this, we introduce a new deep neural network called CSAC-Net. Firstly, we extract the time-frequency feature from the raw signal, which contains rich information. Secondly, we design a convolutional neural network supplemented by an attention mechanism for further feature extraction. Additionally, we propose to utilize model-agnostic meta-learning to adapt to new subjects and this learning strategy achieves better results than the state-of-the-art methods. By the basic experiment on CapgMyo and three ablation studies, we demonstrate the advancement of CSAC-Net.

Measuring Biosignals with Single Circuit Boards

To measure biosignals constantly, using textile-integrated or even textile-based electrodes and miniaturized electronics, is ideal to provide maximum comfort for patients or athletes during monitoring. While in former times, this was usually solved by integrating specialized electronics into garments, either connected to a handheld computer or including a wireless data transfer option, nowadays increasingly smaller single circuit boards are available, e.g., single-board computers such as Raspberry Pi or microcontrollers such as Arduino, in various shapes and dimensions. This review gives an overview of studies found in the recent scientific literature, reporting measurements of biosignals such as ECG, EMG, sweat and other health-related parameters by single circuit boards, showing new possibilities offered by Arduino, Raspberry Pi etc. in the mobile long-term acquisition of biosignals. The review concentrates on the electronics, not on textile electrodes about which several review papers are available.

Long-Term Myoelectric Training with Delayed Feedback in the Home Environment

Myoelectric prosthesis users typically do not receive immediate feedback from their device. They must be able to consistently produce distinct muscle activations in the absence of augmented feedback. In previous experiments, abstract decoding has provided real-time visual feedback for closed loop control. It is unclear if the performance in those experiments was due to short-term adaptation or motor learning. To test if similar performance could be reached without short-term adaptation, we trained participants with a delayed feedback paradigm. Feedback was delayed until after the ~1.5 s trial was completed. Three participants trained for five days in their home environments, completing a cumulative total of 4920 trials. Participants became highly accurate while receiving no real-time feedback of their control input. They were also able to retain performance gains across days. This strongly suggests that abstract decoding with delayed feedback facilitates motor learning, enabling four class control without immediate feedback.

Co-creation and User Perspectives for Upper Limb Prosthetics

People who either use an upper limb prosthesis and/or have used services provided by a prosthetic rehabilitation centre, experience limitations of currently available prosthetic devices. Collaboration between academia and a broad range of stakeholders, can lead to the development of solutions that address peoples' needs. By doing so, the rate of prosthetic device abandonment can decrease. Co-creation is an approach that can enable collaboration of this nature to occur throughout the research process. We present findings of a co-creation project that gained user perspectives from a user survey, and a subsequent workshop involving: people who use an upper limb prosthesis and/or have experienced care services (users), academics, industry experts, charity executives, and clinicians. The survey invited users to prioritise six themes, which academia, clinicians, and industry should focus on over the next decade. The prioritisation of the themes concluded in the following order, with the first as the most important: function, psychology, aesthetics, clinical service, collaboration, and media. Within five multi-stakeholder groups, the workshop participants discussed challenges and collaborative opportunities for each theme. Workshop groups prioritised the themes based on their discussions, to highlight opportunities for further development. Two groups chose function, one group chose clinical service, one group chose collaboration, and another group chose media. The identified opportunities are presented within the context of the prioritised themes, including the importance of transparent information flow between all stakeholders; user involvement throughout research studies; and routes to informing healthcare policy through collaboration. As the field of upper limb prosthetics moves toward in-home research, we present co-creation as an approach that can facilitate user involvement throughout the duration of such studies.

Co-Creation Facilitates Translational Research on Upper Limb Prosthetics

People who either use an upper limb prosthesis and/or have used services provided by a prosthetic rehabilitation centre, hereafter called users, are yet to benefit from the fast-paced growth in academic knowledge within the field of upper limb prosthetics. Crucially over the past decade, research has acknowledged the limitations of conducting laboratory-based studies for clinical translation. This has led to an increase, albeit rather small, in trials that gather real-world user data. Multi-stakeholder collaboration is critical within such trials, especially between researchers, users, and clinicians, as well as policy makers, charity representatives, and industry specialists. This paper presents a co-creation model that enables researchers to collaborate with multiple stakeholders, including users, throughout the duration of a study. This approach can lead to a transition in defining the roles of stakeholders, such as users, from participants to co-researchers. This presents a scenario whereby the boundaries between research and participation become blurred and ethical considerations may become complex. However, the time and resources that are required to conduct co-creation within academia can lead to greater impact and benefit the people that the research aims to serve.