This study introduces a new EMG-based muscle fatigue index that combines EMG signal features in spatial, temporal, and spectral domains. This index incorporates a novel spatial EMG feature: Eigen Ratio Based Fatigue Index (ERFI) that can capture the variations in the active motor unit distribution during dynamic muscle fatiguing exercises. The new fatigue index maps the ERFI, a wavelet-based feature, a spectral feature, and an amplitude-based feature to the reduction in maximum voluntary contraction, which is considered the direct measure of muscle fatigue. The mapping function was implemented as a Multi-layer Perceptron (MLP). To evaluate the fatigue index, several fatigue tests under various speed and load conditions were conducted on four subjects. ERFI showed a significant variation (p < 0.01) over time for more than 85 % of the tests. Several MLP input configurations to predict muscle fatigue were compared in this study based on various combinations of EMG features. An input configuration that used the novel spatial EMG feature among five other features performed the best and was able to predict muscle fatigue with a mean coefficient of determination over 65 %. It was also noticed that ERFI’s relationship with muscle fatigue is less dependent on the load and speed of the cyclic exercise when compared with other EMG features that were proposed in previous studies. Thus it can be considered a better alternative to use in EMG-based control of active prosthetic and orthotic devices to compensate for the effect of muscle fatigue.

A patient would contract surface muscles as a reaction called muscle guarding when experiencing discomfort and pain during physical palpation. This reaction carries important information about an affected location. Training physicians to regulate palpation forces to elicit just enough muscle tension is a challenge using real patients. Tunable stiffness mechanisms enabled by soft robotics can be effectively integrated into medical simulator designs for effective clinical education. In this paper, we propose a controllable stiffness muscle layer to simulate guarding for abdominal palpation training. Designs with soft, fine, and rigid granular jamming, stretchable and non-stretchable layer jamming mechanisms were tested and evaluated as methods to create controllable stiffness muscle. User studies have been carried out on 10 naive participants to differentiate the tense and relaxed abdomen with the proposed jamming mechanisms. Muscle samples made of ground coffee (fine granular jamming) and latex layers (stretchable layer jamming) show good usability in simulating abdomen with different stiffness with at least 75% of the user data exhibits more than 70% of decision accuracy for both tested palpation gestures (single finger and multiple fingers) after short pre-training.

Accurate and real-time hand gesture recognition is essential for controlling advanced hand prostheses. Surface Electromyography (sEMG) signals obtained from the forearm are widely used for this purpose. Here, we introduce a novel hand gesture representation called Temporal Muscle Activation (TMA) maps which captures information about the activation patterns of muscles in the forearm. Based on these maps, we propose an algorithm that can recognize hand gestures in real-time using a Convolution Neural Network. The algorithm was tested on 8 healthy subjects with sEMG signals acquired from 8 electrodes placed along the circumference of the forearm. The average classification accuracy of the proposed method was 94%, which is comparable to state-of-the-art methods. The average computation time of a prediction was 5.5ms, making the algorithm ideal for the real-time gesture recognition applications.

Surface electromyography (sEMG) is a popular bio-signal used for controlling prostheses and finger gesture recognition mechanisms. Myoelectric prostheses are costly, and most commercially available sEMG acquisition systems are not suitable for real-time gesture recognition. In this paper, a method of acquiring sEMG signals using novel low-cost, active, dry-contact, flexible sensors has been proposed. Since the active sEMG sensor was developed to be used along with a bionic arm, the sensor was tested for its ability to acquire sEMG signals that could be used for real-time classification of five selected gestures. In a study of 4 subjects, the average classification accuracy for real-time gesture classification using the active sEMG sensor system was 85%. The common-mode rejection ratio of the sensor was measured to 59 dB, and thus the sensor's performance was not substantially limited by its active circuitry. The proposed sensors can be interfaced with a variety of amplifiers to perform fully wearable sEMG acquisition. This satisfies the need for a low-cost sEMG acquisition system for prostheses.

Assistive devices for disabled individuals provide the support to fulfil their activities of daily living. The proposed mobile robotic assistant (MRA) in this paper is capable of providing both mobile and manipulation support for users. The MRA, which consists of an electric wheelchair and a custom developed 5DOF robotic manipulator is controlled by a Motor Imagery (MI) Electroencephalography (EEG) and Electrooculography (EOG) based Brain Machine Interface (BMI) which is proposed in this paper. A custom developed Graphical User Interface (GUI) is utilized to interact with the users and users are able to control either the wheelchair or the robotic manipulator based on a combination of left and right hand MI-EEG signals and EOG signals via this GUI. A Multilayer Perceptron (MLP) Neural Network based classifier is developed to classify the EEG signals of left vs right vs rest. EOG signals (eye-blinks) are used to activate the task on the GUI menu. A set of experiments have been carried out with healthy subjects and the results show the effectiveness of the proposed methods.

A Meal Assistance Robot is an assistive device that is used to aid individuals who cannot independently direct food to their mouths for consuming. For individuals who undergo loss of upper limb functions due to amputations, spinal cord injuries or cerebral palsy, self-feeding can be impossible, and to assist such individuals in regaining their independence meal assistance robots have been introduced. In this paper we propose a meal assistance robot that is controlled using user intentions based on Electroencephalography (EEG) signals while incorporating camera-based automatic mouth position tracking and mouth open detection systems. In the proposed system, users select any solid food item that they desire to consume from three different containers by looking at corresponding flickering LED matrices. User intentions are identified through EEG signals using a Steady State Visual Evoked Potentials (SSVEP) based intention detection method. Initial motion commands for scooping food from the containers are generated and sent to the meal assistance robot from this first stage. At the second stage, a camera-based mouth position tracking method is proposed for automatically detecting the user's mouth position and thereby moving the spoon or end-effector of the meal assistance robot towards the mouth of the user. This method is capable of automatically tracking the mouth position of users irrespective of their individual body differences and seating positions. A mouth open/closed recognition method is implemented at the final stage in order to feed food to the users when they desire consumption, indicated by the opening/closing of their mouth. A set of experiments were carried out with healthy subjects to validate the proposed system and results are here presented.

Lack of forearm muscles in transhumerai amputees is one of the major issues when it comes to control of upper-limb prosthetic arms using only EMG signals. Brain signal is one of the alternative input signals that can be explored to recognize the motion intention of the users and subsequently can be used to control the upper-limb prosthetic hands. This paper proposes a transhumeral prosthetic arm which is controlled using both EMG and EEG signals. The proposed transhumeral prosthetic arm consists of 1 DOF elbow joint and 6 DOF prosthetic hand called UOMPro. In the proposed system, the elbow joint flexion/extension motions are realized based on EMG signals of biceps and triceps muscles. A real time fuzzy-neuro network based method is used for controlling the elbow joint of the prosthetic arm using EMG signals. In order to control the hand open/close motions of the hand prosthetic, EEG-signal based approach is proposed. The proposed EEG-based approach is based on Steady State Visual Evoked Potentials (SSVEP) where user can use his/her brain signals to open/close the hand prosthetic. A simple yet effective threshold based algorithm is used recognize the SSVEP signal levels and results are mapped in to a low-level controller of the hand prosthetic. A set of experiments are conducted with healthy subjects to validate the performance of the proposed system and results are presented with potential future directions.

Meal assistance robots provide disabled individuals the access to one of the important activities in daily living, self-feeding. This paper proposes a Steady State Visually Evoked Potential (SSVEP) based Brain Machine Interface (BMI) for controlling of a meal assistance robot. In the proposed system, the user has the facility to select any solid food item that he would like to eat from 3 different bowls just by looking at the respective LED matrices blinking at different frequencies. The generated SSVEP signals while looking at the LEDs are extracted from EEG signals acquired using OpenBCI EEG signal acquisition system. Extracted SSVEP signals are used to identify the intention of the user and subsequently the detected intentions are used to operate the meal assistant robot. Experiments are carried out to validate the system and results indicate the effectiveness of the proposed method.