Deep Learning-Based Identification Algorithm for Transitions Between Walking Environments Using Electromyography Signals Only.

Although studies on terrain identification algorithms to control walking assistive devices have been conducted using sensor fusion, studies on transition classification using only electromyography (EMG) signals have yet to be conducted. Therefore, this study was to suggest an identification algorithm for transitions between walking environments based on the entire EMG signals of selected lower extremity muscles using a deep learning approach. The muscle activations of the rectus femoris, vastus medialis and lateralis, semitendinosus, biceps femoris, tibialis anterior, soleus, medial and lateral gastrocnemius, flexor hallucis longus, and extensor digitorum longus of 27 subjects were measured while walking on flat ground, upstairs, downstairs, uphill, and downhill and transitioning between these walking surfaces. An artificial neural network (ANN) was used to construct the model, taking the entire EMG profile during the stance phase as input, to identify transitions between walking environments. The results show that transitioning between walking environments, including continuously walking on a current terrain, was successfully classified with high accuracy of 95.4 % when using all muscle activations. When using a combination of muscle activations of the knee extensor, ankle extensor, and metatarsophalangeal flexor group as classifying parameters, the classification accuracy was 90.9 %. In conclusion, transitioning between gait environments could be identified with high accuracy with the ANN model using only EMG signals measured during the stance phase.

Design and investigation of the effectiveness of a metatarsophalangeal assistive device on the muscle activities of the lower extremity.

The metatarsophalangeal (MTP) joint is not considered in most current walking assistive devices even though it plays an important role during walking. The purpose of this study was to develop a new MTP assistive device and investigate its effectiveness on the muscle activities of the lower extremities during walking while wearing the device. The MTP assistive device is designed to support MTP flexion by transmitting force through a cable that runs parallel with the plantar fascia. Eight participants were instructed to walk at a constant speed on a treadmill while wearing the device. The muscle activities of their lower extremities and MTP joint kinematics were obtained during walking under both actuated and non-actuated conditions. Paired t-tests were performed to compare the differences in each dependent variable between the two conditions. The muscle activity of the MTP flexor was significantly reduced during walking under actuated conditions (p = 0.013), whereas no differences were found in the muscle activities of other muscles or in the MTP joint angle between actuated and non-actuated conditions (p > 0.05 for all comparisons). In conclusion, the cable-driven MTP assistive device is able to properly assist the MTP flexor without interfering with the action of other muscles in the lower extremities; as such, this MTP assistive device, when integrated into existing exoskeleton designs, has the potential to offer improved walking assistance by reducing the amount of muscle activity needed from the MTP flexor.

Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only.

Classification of terrain is a vital component in giving suitable control to a walking assistive device for the various walking conditions. Although surface electromyography (sEMG) signals have been combined with inputs from other sensors to detect walking intention, no study has yet classified walking environments using sEMG only. Therefore, the purpose of this study is to classify the current walking environment based on the entire sEMG profile gathered from selected muscles in the lower extremities. The muscle activations of selected muscles in the lower extremities were measured in 27 participants while they walked over flat-ground, upstairs, downstairs, uphill, and downhill. An artificial neural network (ANN) was employed to classify these walking environments using the entire sEMG profile recorded for all muscles during the stance phase. The result shows that the ANN was able to classify the current walking environment with high accuracy of 96.3% when using activation from all muscles. When muscle activation from flexor/extensor groups in the knee, ankle, and metatarsophalangeal joints were used individually to classify the environment, the triceps surae muscle activation showed the highest classification accuracy of 88.9%. In conclusion, a current walking environment was classified with high accuracy using an ANN based on only sEMG signals.