A New Force Myography-Based Approach for Continuous Estimation of Knee Joint Angle in Lower Limb Amputees and Able-Bodied Subjects

Biosignal-based transferable attention Bi-ConvGRU deep network for hand-gesture recognition towards online upper-limb prosthesis control

BACKGROUND AND OBJECTIVE

Upper-limb amputation can significantly affect a person's capabilities with a dramatic impact on their quality of life. As a biological signal, surface electromyogram (sEMG) provides a non-invasive means to measure underlying muscle activation patterns, corresponding to specific hand gestures. This project aims to develop a real-time deep learning based recognition model to automatically and reliably recognise these complex signals of a wide range of daily hand gestures from amputees and non-amputees.

METHODS

This paper proposes an attention bidirectional Convolutional Gated Recurrent Unit (Bi-ConvGRU) deep neural network for hand-gesture recognition. By training on sEMG data from both amputees and non-amputees, the model can learn to recognise a group of fine-grained hand movements. This is a significantly more challenging and underexplored area, compared to existing studies on coarse-control in lower limbs. One dimensional CNNs are initially used to extract intra-channel features. The novel use of a bidirectional sequential GRU (Bi-GRU) deep neural network allows the exploration of correlation of muscle activation among multi-channel sEMG signals from both prior and posterior time sequences. Importantly, the attention mechanism is employed following Bi-GRU layers. This enables the model to learn vital parts and feature weights, increasing robustness to bio-data noise and irregularity. Finally, we introduce the first of its kind transfer learning, demonstrating that a baseline model pre-trained with non-amputee data can be effectively refined with amputee data to build a personalised model for amputees.

RESULTS

The attention Bi-ConvGRU was evaluated on the benchmark database Ninapro, and achieved an average accuracy of 88.7%, outperforming the state-of-the-art on 18 gesture recognition by 6.7%.

CONCLUSIONS

To our knowledge, the developed end-to-end deep learning model is the first of its kind that enables reliable predictive decision making in short time windows (160ms). This reduced latency limits physiological awareness, enabling the potential for real-time, online and thus more intuitive bio-control of prosthetic devices for amputees.

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG)-, force myography (FMG)-, and electrical impedance tomography (EIT)-based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios.

Deep Learning Networks for View-independent Knee and Elbow Joint Angle Estimation

Vision-based human joint angle estimation is essential for remote and continuous health monitoring. Most vision-based angle estimation methods use the locations of human joints extracted using optical motion cameras, depth cameras, or human pose estimation models. This study aimed to propose a reliable and straightforward approach with deep learning networks for knee and elbow flexion/extension angle estimation from the RGB video. Fifteen healthy participants performed four daily activities in this study. The experiments were conducted with four different deep learning networks, and the networks took nine subsequent frames as input while output was knee and elbow joint angles extracted from an optical motion capture system for each frame. The BiLSTM network-based joint angles estimator can estimate both joint angles with a correlation of 0.955 for knee and 0.917 for elbow joints regardless of the camera view angles.

Continuous Myoelectric Prediction of Future Ankle Angle and Moment Across Ambulation Conditions and Their Transitions

A hallmark of human locomotion is that it continuously adapts to changes in the environment and predictively adjusts to changes in the terrain, both of which are major challenges to lower limb amputees due to the limitations in prostheses and control algorithms. Here, the ability of a single-network nonlinear autoregressive model to continuously predict future ankle kinematics and kinetics simultaneously across ambulation conditions using lower limb surface electromyography (EMG) signals was examined. Ankle plantarflexor and dorsiflexor EMG from ten healthy young adults were mapped to normal ranges of ankle angle and ankle moment during level overground walking, stair ascent, and stair descent, including transitions between terrains (i.e., transitions to/from staircase). Prediction performance was characterized as a function of the time between current EMG/angle/moment inputs and future angle/moment model predictions (prediction interval), the number of past EMG/angle/moment input values over time (sampling window), and the number of units in the network hidden layer that minimized error between experimentally measured values (targets) and model predictions of ankle angle and moment. Ankle angle and moment predictions were robust across ambulation conditions with root mean squared errors less than 1° and 0.04 Nm/kg, respectively, and cross-correlations (R²) greater than 0.99 for prediction intervals of 58 ms. Model predictions at critical points of trip-related fall risk fell within the variability of the ankle angle and moment targets (Benjamini-Hochberg adjusted p > 0.065). EMG contribution to ankle angle and moment predictions occurred consistently across ambulation conditions and model outputs. EMG signals had the greatest impact on noncyclic regions of gait such as double limb support, transitions between terrains, and around plantarflexion and moment peaks. The use of natural muscle activation patterns to continuously predict variations in normal gait and the model's predictive capabilities to counteract electromechanical inherent delays suggest that this approach could provide robust and intuitive user-driven real-time control of a wide variety of lower limb robotic devices, including active powered ankle-foot prostheses.

Optimal Force Myography Placement For Maximizing Locomotion Classification Accuracy in Transfemoral Amputees: A Pilot Study

Force myography (FMG), is shown to be a promising alternative to electromyography in locomotion classification. However, the placement of force myography sensors over the thigh during locomotion is not yet clear. To this end, an inhouse developed FMG strap was placed over the thigh muscles of healthy/amputees, while walking on different terrains. The performance of the system was tested on six healthy and two amputees during the five different placements of FMG strap i.e., base, distal, lateral, medial, and proximal. The study reveals that there is an increase in average accuracy (STD) from [mean (STD)] 96.4% (4.0) to 99.5% (0.5) for healthy individuals and 95.5% (3.0) to 99.1% (0.3) for amputees while moving the FMG strap to the proximal of the thigh/stump. The study further determines the combination of three FMG channels on anterior side (Rectus Femoris, Vastus lateralis, and Iliotibial Tract muscles) that provides classification accuracy at par (p > 0.05) to utilizing all eight channels for locomotion classification. The variation of humidity throughout the trials did not significantly (p > 0.05) affect the classification accuracy. The study concludes that the optimal location to place the FMG strap is proximal to the thigh/ stump with a minimum of three FMG channels on the anterior part of the thigh for superior classification accuracy.