Up to one's knees in data: Data-driven intent recognition using electromyography for the lower limb

Data-driven intent recognition using electromyography (EMG) has the potential to make actuated prosthesis more intuitive. Although electromyography has its challenges, it also forms an opportunity to realize more intuitive control of prostheses through intent recognition.
However, data-driven approaches require a lot of data. Therefore, large scale data collection needed to be facilitated. In chapter 2 we developed and validated a synchronization method for wearable motion capture and EMG measurement systems.
Using this new synchronization technique we collected a large database, MyPredict, one of the biggest of its kind, containing 55 able-bodied subjects measured in 85 measurement moments. These data sets contain kinematic and EMG data from subjects performing gait-related activities such as stair climbing and ramp walking, freely transitioning from one activity to the next.
We investigated the use of genetic algorithms to construct optimized feature sets to be used in lower limb prosthetic control in chapter 4. The results of these optimizations showcase the possibilities of data-driven feature selection and the potential to optimize current control systems.
Myoelectric pattern recognition systems suffer from increase in error rate over time, which could lead to unwanted behavior. This so-called concept drift in myoelectric control systems could be caused by fatigue, sensor replacement and varying skin conditions. In chapter 5 we investigated whether concept drift is an issue in lower limb pattern recognition. We concluded that an adaptation strategy is necessary as the baseline error rate significantly increased from day 1 to day 2.
Direct control using surface electromyography enables more intuitive control of a transfemoral prosthesis. In chapter 6 we compared three different modelling frameworks to estimate knee torque in non-weight-bearing situations. Here we concluded that a data-driven convolutional neural network performed best overall.
Summarizing, many data-driven approaches are suitable to increase the performance of intent recognition systems compared to state-of-the-art. However, the amount of data and standardization of evaluation protocols remains a critical point of attention moving forward.