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A new non-invasive and flexible sensor was fabricated to monitor the muscular activity of upper body muscles. It consists of a dry electrode made of multimaterial metal-polymer-glass hollow-core fiber electrodes connected to a custom made signal acquisition platform. This fiber electrode only adds a negligible contribution to the recording system’s internal noise ($7.626~\mu \text{V}$ peak-to-peak and $1.234~\mu \text{V}$ RMS; recording system noise: $6.901~\mu \text{V}$ peak-to-peak and $1.205~\mu \text{V}$ RMS). Surface electromyograms (sEMGs) of the biceps bracci and trapezius muscles were recorded during maximal voluntary contractions using the new sensor and compared to that obtained with medical grade commercial sensors. Frequency content and time domain analysis show that the new flexible sensor performs similarly to commercial devices in terms of sEMG signal amplitude discrimination and frequency shift evaluation during the development of muscle fatigue. The advantage of the new sensor is the high flexibility of the fibers allowing their easy integration into stretchable garments without compromising the user’s comfort. This will lead to the development of a new generation of smart textile with large application for telemedicine and assistive devices. [ABSTRACT FROM AUTHOR]

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This paper’s hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised 19 and 17 able-bodied participants, respectively (the first one is employed for pre-training), were recorded for this work, using the Myo armband. A third Myo armband dataset was taken from the NinaPro database and is comprised ten able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, spectrograms, and continuous wavelet transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time. [ABSTRACT FROM AUTHOR]

Wireless body sensors are increasingly used by clinicians and researchers in a wide range of applications, such as sports, space engineering, and medicine. Monitoring vital signs in real time can dramatically increase diagnosis accuracy and enable automatic curing procedures, e.g., detect and stop epilepsy or narcolepsy seizures. Breathing parameters are critical in oxygen therapy, hospital, and ambulatory monitoring, while the assessment of cough severity is essential when dealing with several diseases, such as chronic obstructive pulmonary disease. In this paper, a low-power wireless respiratory monitoring system with cough detection is proposed to measure the breathing rate and the frequency of coughing. This system uses wearable wireless multimodal patch sensors, designed using off-the-shelf components. These wearable sensors use a low-power nine-axis inertial measurement unit to quantify the respiratory movement and a MEMs microphone to record audio signals. Data processing and fusion algorithms are used to calculate the respiratory frequency and the coughing events. The architecture of each wireless patch-sensor is presented. In fact, the results show that the small $26.67 \times 65.53$ mm2 patch-sensor consumes around 12–16.2 mA and can last at least 6 h with a miniature 100-mA lithium ion battery. The data processing algorithms, the acquisition, and wireless communication units are described. The proposed network performance is presented for experimental tests with a freely behaving user in parallel with the gold standard respiratory inductance plethysmography. [ABSTRACT FROM AUTHOR]

This paper describes a multimodal body–machine interface (BoMI) to help individuals with upper-limb disabilities using advanced assistive technologies, such as robotic arms. The proposed system uses a wearable and wireless body sensor network (WBSN) supporting up to six sensor nodes to measure the natural upper-body gesture of the users and translate it into control commands. Natural gesture of the head and upper-body parts, as well as muscular activity, are measured using inertial measurement units (IMUs) and surface electromyography (sEMG) using custom-designed multimodal wireless sensor nodes. An IMU sensing node is attached to a headset worn by the user. It has a size of 2.9 cm $\times$ 2.9 cm, a maximum power consumption of 31 mW, and provides angular precision of 1 $^\circ$. Multimodal patch sensor nodes, including both IMU and sEMG sensing modalities are placed over the user able-body parts to measure the motion and muscular activity. These nodes have a size of 2.5 cm $\times$ 4.0 cm and a maximum power consumption of 11 mW. The proposed BoMI runs on a Raspberry Pi. It can adapt to several types of users through different control scenarios using the head and shoulder motion, as well as muscular activity, and provides a power autonomy of up to 24 h. JACO, a 6-DoF assistive robotic arm, is used as a testbed to evaluate the performance of the proposed BoMI. Ten able-bodied subjects performed ADLs while operating the AT device, using the Test d’Évaluation des Membres Supérieurs de Personnes Âgées to evaluate and compare the proposed BoMI with the conventional joystick controller. It is shown that the users can perform all tasks with the proposed BoMI, almost as fast as with the joystick controller, with only 30% time overhead on average, while being potentially more accessible to the upper-body disabled who cannot use the conventional joystick controller. Tests show that control performance with the proposed BoMI improved by up to 17% on average, after three trials. [ABSTRACT FROM AUTHOR]

Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO. [ABSTRACT FROM AUTHOR]

Increasing performance while decreasing the cost of sEMG prostheses is an important milestone in rehabilitation engineering. The different types of prosthetic hands that are currently available to patients worldwide can benefit from more effective and intuitive control. This paper presents a real-time approach to classify finger motions based on surface electromyography (sEMG) signals. A multichannel signal acquisition platform implemented using components off the shelf is used to record forearm sEMG signals from 7 channels. sEMG pattern classification is performed in real time, using a Linear Discriminant Analysis approach. Thirteen hand motions can be successfully identified with an accuracy of up to 95.8% and of 92.7% on average for 8 participants, with an updated prediction every 192 ms.

In the realm of Human Activity Recognition (HAR), supervised machine learning and deep learning are commonly used. Their training is done using time and frequency features extracted from raw data (inertial and gyroscopic). Nevertheless, raw data are seldom employed. In this paper, a dataset of able-bodied participants is recorded using 3 custom wireless motion sensors providing embedded IMU and sEMG detection and processing and a base station (a Raspberry Pi 3) running a classification algorithm. A Support Vector Machine with Radius Basis Function Kernel (RBF-SVM) is augmented using Spherical Normalization to achieve a motion classification accuracy of 97.35% between 8 body motions. The proposed classifier allows for real-time prediction callback with low latency output.