Low-cost wearable multichannel surface EMG acquisition for prosthetic hand control

Abstract

Abstract-Prosthetic hand control based on the acquisition and processing of surface electromyography signals (sEMG) is a well-established method that makes use of the electric potentials evoked by the physiological contraction processes of one or more muscles. Furthermore intelligent mobile medical devices are on the brink of introducing safe and highly sophisticated systems to help a broad patient community to regain a considerable amount of life quality. The major challenges which are inherent in such integrated system's design are mainly to be found in obtaining a compact system with a long mobile autonomy, capable of delivering the required signal requirements for EMG based prosthetic control with up to 32 simultaneous acquisition channels and -with an eye on a possible future exploitation as a medical device -a proper perspective on a low priced system. Therefore, according to these requirements we present a wireless, mobile platform for acquisition and communication of sEMG signals embedded into a complete mobile control system structure. This environment further includes a portable device such as a laptop providing the necessary computational power for the control and a commercially available robotic handprosthesis. Means of communication among those devices are based on the Bluetooth standard. We show, that the developed low cost mobile device can be used for proper prosthesis control and that the device can rely on a continuous operation for the usual daily life usage of a patient.

Assessment of a Wearable Force- and Electromyography Device and Comparison of the Related Signals for Myocontrol

“…Figure 1 (left panel) shows the system as worn by a subject: the device is composed of three modules: a set of mixed sEMG/FMG sensors (in this case, arranged on two Velcro bracelets), a Bluetooth analog-to-digital conversion board gathering and transmitting the signals, and a smartphone receiving the data via Bluetooth and able to perform myocontrol via a machine learning algorithm. The board was based upon the work of Brunelli et al (2015), whereas the learning algorithm is Incremental Ridge Regression with Random Fourier Features (see below for more details), already been evaluated (in a non-wearable control system) by Gijsberts et al (2014) and Strazzulla et al (2016). Although not extensively used in this specific experiment, the machine learning algorithm can produce control signals in real time and transmit them to the sensor board, which serves as a relay routing them to a hand prosthetic device connected to it.…”

Stand-Alone Wearable System for Ubiquitous Real-Time Monitoring of Muscle Activation Potentials

“…Multichannel operation is possible, in order to acquire signals from more than one muscle at the same time. Examples of multichannel systems are reported in [ 13 , 14 ], where electrodes are positioned all around the forearm for hand motion recognition. In [ 14 ], an innovative two electrode configuration is proposed, sharing one electrode in adjacent channels.…”

Hand Movement Classification Using Burg Reflection Coefficients

“…All these applications need preprocessing of signals and its extraction of features [6]. sEMG are useful as input control signals for prosthetic limbs [7,8], in rehabilitation as a measurement parameter of muscular effort [9], and for the development of muscle machine interfaces [10].…”