A Method for Locomotion Mode Identification Using Muscle Synergies

Abstract

Active lower limb transfemoral prostheses have enabled amputees to perform different locomotion modes such as walking, stair ascent, stair descent, ramp ascent and ramp descent. To achieve seamless mode transitions, these devices either rely on neural information from the amputee's residual limbs or sensors attached to the prosthesis to identify the intended locomotion modes or both. We present an approach for classification of locomotion modes based on the framework of muscle synergies underlying electromyography signals. Neural information at the critical instances (e.g., heel contact and toe-off) was decoded for this purpose. Non-negative matrix factorization was used to extract the muscles synergies from the muscle feature matrix. The estimation of the neural command was done using non-negative least squares. The muscle synergy approach was compared with linear discriminant analysis (LDA), support vector machine (SVM), and neural network (NN) and was tested on seven able-bodied subjects. There was no significant difference ( p > 0.05 ) in transitional and steady state classification errors during stance phase. The muscle synergy approach performed significantly better ( ) than NN and LDA during swing phase while results were similar to SVM. These results suggest that the muscle synergy approach can be used to discriminate between locomotion modes involving transitions.

A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

“…The participants were instructed to walk a forty-meter flat ground four times, and then walked back and forth twenty times on a slope with a length of 5 m angled at 5.2 degrees. Similar ramp angle degrees are also found in other relevant studies and they are summarized in Table 2 for comparison with our work [19,22,27,31,35]. Table 2.…”

“…Ramp Angle [22] 4.78 degree [27] 10 degree [31] 10 degree [19] 12 degree [35] 8.5 degree This work 5.2 degree…”

“…In recent years, terrain classification techniques enable wearable assistant systems to recognize more terrains, which allows patients to perform rehabilitation training in more complex scenarios, and therefore has aroused wide attention [17][18][19]. Among all terrains, uphill, level ground, and downhill are the three most common terrains in daily life, and thus are widely investigated in the study of terrain identification [19][20][21][22].…”

“…In [27], signals from 13 IMUs on the robotic legs of six transfemoral amputee subjects were collected and used to classify different terrains and an average accuracy of 81.2% was reached in recognizing ramp and level ground. In [22], the authors proposed a terrain identification method, namely muscle synergies, based on the EMG signals from twelve muscles. The data from seven able-bodied participants were collected and were classified as level ground and ramp with an average accuracy of 83.8%.…”

Flexible and Wearable GRF and EMG Sensors Enabled Locomotion Mode Recognition for IoHT Based In-home Rehabilitation

“…A possible explanation for this is that, in some particular cases, like accidentally stepping out with an unbalanced stride, the human body will adjust naturally to avoid falling down, resulting in unusual changes in the EMG and GRF patterns and possible misjudgment of the classifier. This phenomenon (i.e., the error rate of recognizing RD into LW) is higher than the overall error rate and is also found in relevant works [27][28][29] (a comparison is given in Table II). A potential solution is to enlarge the volume of the training dataset.…”