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Abstract
A hardware/software co-design for assessing post-Anterior Cruciate Ligament (ACL) reconstruction ambulation is presented. The knee kinematics and neuromuscular data during walking (2–6 km h−1) have been acquired using wireless wearable motion and electromyography (EMG) sensors, respectively. These signals were integrated by superimposition and mixed signals processing techniques in order to provide visual analyses of bio-signals and identification of the recovery progress of subjects. Monitoring overlapped signals simultaneously helps in detecting variability and correlation of knee joint dynamics and muscles activities for an individual subject as well as for a group. The recovery stages of subjects have been identified based on combined features (knee flexion/extension and EMG signals) using an adaptive neuro-fuzzy inference system (ANFIS). The proposed system has been validated for 28 test subjects (healthy and ACL-reconstructed). Results of ANFIS showed that the ambulation data can be used to distinguish subjects at different levels of recuperation after ACL reconstruction.
Acknowledgement
This work is supported by the University Research Council (URC) grant scheme at the Universiti Brunei Darussalam under the grant No: UBD/PNC2/2/RG/1(195). with the title ‘Integrated Motion Analysis System (IMAS)’. The authors appreciate the sports medicine centre at ministry of sports and performance optimization centre at ministry of defence, Brunei Darussalam for providing Brunei national athletes as test subjects who had undergone the rehabilitation process due to ACL surgeries as well as healthy test subjects involved for non-invasive rehabilitation experiments. Further, the authors also acknowledge the support provided by Mr Illepurma Ranasinghe (head physical strength and conditioning) and Ms Maria Leah (physiotherapist) from the performance optimization centre at the Ministry of Defence.