Electromyography data for non-invasive naturally-controlled robotic hand prostheses

Abstract

Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs. This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark scientific database. The database is targeted at studying the relationship between surface electromyography, hand kinematics and hand forces, with the final goal of developing non-invasive, naturally controlled, robotic hand prostheses. The validation section verifies that the data are similar to data acquired in real-life conditions, and that recognition of different hand tasks by applying state-of-the-art signal features and machine-learning algorithms is possible.

Deep learning-based artificial vision for grasp classification in myoelectric hands

“…These workaround techniques have emerged mainly because the promised EMG pattern recognition-based methods have not proved robust, or even feasible, for grasp classification clinically. The non-intuitiveness and shortcomings of the aforementioned approaches have encouraged the emergence of techniques that advocate utilisation of sensing modalities other than the conventional EMG signals, such as accelerometry or in general inertial measurements [14,25,26,57], RFID tags [28], artificial vision including standard cameras as well as Kinect [29][30][31][32][33][34]. In almost all multi-modal approaches to control limb prosthesis, it is argued that the incorporation of two or more sources of information can reduce the users' cognitive burden and enhance functionality in terms of accuracy.…”

“…As intermediate solutions, alternative modalities have been adopted to replace or augment the EMG signals. Skin movement analysis via accelerometry signals [25,26], force myography [27], use of radio-frequency identification (RFID) tags [28], arm movement trajectory and inertial measurement (e.g. i-mo TM ) and computer vision [29][30][31][32][33][34] are some examples.…”

sEMG-Based Gesture Recognition with Convolution Neural Networks

“…The more extended window lengths led to higher controller delays as well as increased classification accuracy [42][43][44]. In previous works [13,40,45], L is greater than 200 ms to get higher classification accuracy. To test the performance of the proposed algorithm in this study, L equal to 100 ms was chosen.…”

“…Before the raw data could be used, those signals were processed by several steps such as filtering using a Hampel filter (cleaning the signals from the 50 Hz power-line interference), synchronization and relabeling. The detail can be found in [40].…”

“…In DB2 40 intact subjects (28 males, 12 females; 34 right-handed, 6 left-handed; age 29.9 ± 3.9 years) were instructed to perform 50 types of hand, wrist and functional and grasping movements, organized in three distinct sets of exercises (referred to as Exercises B, C and D in [40]). Each movement was repeated six times with a 3 s rest posture in between.…”