Feature Extraction and Classification of Knee Motions Using a Sensor-Equipped Orthosis

Abstract

This thesis investigates the feasibility of using a sensorized knee orthosis for movement classification and sensor-derived feature extraction during controlled lowerlimb movements. The orthosis was equipped with two six-axis IMU sensors and an absolute capacitive rotary encoder. A movement data-collection study involving 24 healthy participants was conducted, in which four lower-limb movements and three intentionally simulated execution variations were recorded. The collected data were used to train and evaluate three neural network architectures commonly applied to time-series classification: CNN-LSTM, LSTM-CNN, and a parallel architecture. The study is limited to controlled motion analysis using a sensorized wearable system and does not evaluate medical, diagnostic, therapeutic, rehabilitation, or clinical outcomes. The results show that the sensor system provided stable sampling frequencies and data suitable for classification of four movements. The CNN-LSTM model achieved a mean leave-one-subject-out (LOSO) classification accuracy of 99.42 ± 0.35% for the predefined movement classes and more than 99% pseudo-live classification accuracy after post-processing. In addition, movement-related features such as range of motion, knee angle deviations, movement smoothness, and repetition count were extracted from the recorded sensor data. A modified CNN-LSTM architecture was also evaluated for classification of predefined execution categories, demonstrating the feasibility of distinguishing between intentionally defined movement variations. The feature-extraction results should be regarded as exploratory because no external kinematic ground truth was available. Similarly, the execution-category classification represents predefined movement categories rather than a validated assessment of movement quality. Overall, the findings demonstrate the potential of wearable sensor systems and machine learning for controlled movement classification and sensorderived movement analysis.