Abstract— Development of wearable data acquisition systems with applications to human-machine interaction (HMI) is of great interest to assist stroke patients or people with motor disabilities. This paper proposes a hybrid wireless data acquisition system, which combines surface electromyography (sEMG) and inertial measurement unit (IMU) sensors. It is designed to interface wrist extension with external devices, which allows the user to operate devices with hand orientations. A pilot study of the system performed on four healthy subjects has successfully produced two different control signals corresponding to wrist extensions. Preliminary results show a high correlation (0.42-0.75) between sEMG and IMU signals, thus proving the feasibility of such a system. Results also show that the developed system is robust as well as less susceptible to external interferences. The generated control signals can be used to perform real-time control of different devices in daily-life activities, such as turning ON/OFF of lights in a smart home, controlling an electric wheelchair, and other assistive devices. Such a system will help decrease the dependency of disabled people on their caretakers and empower them to perform their daily-life activities independently. I. INTRODUCTION Stroke is one of the leading causes of chronic disability for people and has devastating socioeconomic impacts. Recent studies show that there are about 25.7 million stroke survivors worldwide, and individuals recovering from stroke often experience helplessness and social isolation, which is linked to their decreased ability to manage their daily activities and subsequently, increased depression [1]. Thus, stroke survivors mainly depend on others in their daily routines, making it necessary to design a compact, portable and easy-to-use system that can assist them to lead an independent life. Approximately 65% of stroke patients suffer from hemiparesis, i.e., paralysis or muscular weakness on either side of their body [2]. Thus, the idea is to analyze their healthy hand movements and decode them to operate targeted devices (like smart home appliances, assistive devices, etc.). For such real- time human-machine interaction (HMI) applications, surface electromyography (sEMG) is widely being used. It is an electric signal produced from muscle movements that can be acquired by placing EMG electrodes on the skin surface [3]. Vasylkiv et al. [4] has used sEMG sensors that identified different hand gestures to control smart home lights. Similarly, in [5-7], sEMG signals are used to control rehabilitative Funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 713683 (COFUNDfellowsDTU) 1 M. A. Khan (mahkh@dtu.dk) and R. Das (rigdas@dtu.dk) are Post- Doctoral Researchers at Health Tech. Department of Technical University of Denmark, 2800, Lyngby, Denmark devices. Besides, Yid et al. [8] and Song et al. [9] developed a system to maneuver an electric wheelchair prototype. This system uses the electric potential generated by finger movements [8] and squeezed fist [9]. However, there are several limitations of sEMG based systems, which need to be taken into consideration for future improvement. These systems are sensitive to electrode positioning and prone to sweat conditions and changes in skin impedance [10-11]. To acquire good quality sEMG signals, high-tension movements are required to achieve muscular contraction, causing the muscles fatigue and, thus, deteriorating the performance of the system [4]. Furthermore, there are also challenges related to sEMG signal processing that includes algorithm robustness, adjustment of signal variability, and artifact removal [12]. The aforementioned shortcomings can be overcome by developing a hybrid data acquisition system by combining sEMG and inertial measurement unit (IMU) motion sensors. IMU is a motion-tracking sensor that contains a gyroscope and accelerometer and can determine accurate hand movements. Previous research [13-15] has shown that the combination of sEMG and IMU sensors increases hand movements detection and hand gestures recognition accuracy. Zhang et al. [13] used five sEMG sensors and a 3-axis accelerometer to classify 72 Chinese sign language words. Georgi et al. [14] used 16 sEMG and an IMU sensor to identify 12 different hand gestures. Wolf et al. [15] developed a BioSleeve that contains eight sEMG and an IMU sensor, which is able to classify nine dynamic and 17 static gestures. These studies have shown the promising results; however, none of them is a wireless system and does not generate the control signals for HMI applications. In this work, a pilot study is presented for developing a wearable and wireless data acquisition system for stroke patients. The system contains two sEMG (located on the forearm) and an IMU sensor (located near the wrist) and is easy to use on a daily basis. The system is able to detect different wrist extension angles and muscle potential generated by extensor carpi radialis muscles (primary muscle to perform wrist extension [16]). Based on the recorded data, it sets different threshold levels for sEMG potential, and on achieving the threshold, it generates the respective control signals to wirelessly operate the peripheral devices. Moreover, the integration of wrist extension and EMG signals increases the accuracy for generation of desired control signals. 2 B. M. Bayram is a Master’s Student at Health Tech. Department of Technical University of Denmark, 2800, Lyngby, Denmark 3 Sadasivan Puthusserypady (sapu@dtu.dk) is an Associate Professor at Health Tech. Department of Technical University of Denmark, 2800, Lyngby, Denmark Muhammad Ahmed Khan 1 , Bayram Metin Bayram 2 , Rig Das 1 , IEEE Member, and Sadasivan Puthusserypady 3 , IEEE Senior Member Electromyography and Inertial Motion Sensors Based Wearable Data Acquisition System for Stroke Patients: A Pilot Study 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Oct 31 - Nov 4, 2021. 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