Wireless Multi-Sensor Integration for ACL Rehabilitation Using Biofeedback Mechanism (original) (raw)
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—Anterior cruciate ligament (ACL) trauma, being one of the most common musculoskeletal injuries in sports, leads to knee joint instability and causes ambulation impairments. A careful monitoring of the progress of recovery after ACL reconstruction is crucial for minimizing postoperative complications and rein-juries. This research is aimed at designing a complementary tool to assess the recovery status and knee dynamics during the rehabilitation period after ACL reconstruction. The prototype includes wireless body-mounted motion sensors for kinematics measurements , surface electromyography system for muscle activity measurements, a video camera for recording trial activities and custom-developed intelligent system software that provides classification of the progress of the recovery and visual biofeedback during rehabilitation. The subjects' recovery stages are classified based on combined features from sensors' data, using an adap-tive neuro-fuzzy inference system. The visual biofeedback provides monitoring of different signals simultaneously in order to help in detecting the intra and intersubject variability and correlation between the knee joint dynamics and muscle activities. The promising results of this initial study for assessing the ambulation at various speeds showcase the prospects of using the proposed system as part of existing rehabilitation monitoring procedures to achieve a more effective and timely recovery of ACL-reconstructed subjects.
ACL) reconstruction has been developed in this study. This system provides an objective assessment and monitoring of the rehabilitation progress by integrating 3-D kinematics and neuromuscular signals recorded through wearable motion and electromyography sensors, respectively. The data from a group of healthy and ACL reconstructed subjects were collected for normal/brisk walking (4-6km/h) and single leg balance (eyes open and eyes closed) testing activities. Fuzzy clustering and fuzzy nearest neighbor methods have been used to classify the collected data into different groups for each activity. The classification accuracy of the system is found to be 94.49% for 4 km/h walking speed, 95.41% for 5 km/h walking speed, 96.00% for 6 km/h walking speed, 94.44% for single leg balance testing with eyes open and 95.83% for single leg balance testing with eyes closed. The recovery status of a subject is evaluated based on different activities assessed and the overall assessment is done using ...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2013
An intelligent recovery classification and monitoring system (IRCMS) for post Anterior Cruciate Ligament (ACL) reconstruction has been developed in this study. This system provides an objective assessment and monitoring of the rehabilitation progress by integrating 3-D kinematics and neuromuscular signals recorded through wearable motion and electromyography sensors, respectively. The data from a group of healthy and ACL reconstructed subjects were collected for normal/brisk walking (4-6km/h) and single leg balance (eyes open and eyes closed) testing activities. Fuzzy clustering and fuzzy nearest neighbor methods have been used to classify the collected data into different groups for each activity. The classification accuracy of the system is found to be 94.49% for 4 km/h walking speed, 95.41% for 5 km/h walking speed, 96.00% for 6 km/h walking speed, 94.44% for single leg balance testing with eyes open and 95.83% for single leg balance testing with eyes closed. The recovery status of a subject is evaluated based on different activities assessed and the overall assessment is done using Choquet integral fusion technique. Further, biofeedback mechanism has been developed using a visual monitoring system which provides the variations in strength/activation of knee flexors/extensors and 3-D joint kinematics. This integrated system can be used as an assistive tool by sports trainers, coaches and clinicians for monitoring overall progress of athletes' rehabilitation and classifying their recovery stage for multiple activities.
This paper presents a general framework of intelligent biofeedback for smart healthcare system and its impact on healthcare of professional athletes, especially during rehabilitation monitoring. The application of machine learning techniques along with various wireless wearable sensors facilitated in building a knowledge base system for healthcare monitoring of the subjects and providing a visual/numeric biofeedback to the clinicians, patients and healthcare professionals. The validated system can potentially be used as a decision supporting tool by the clinicians, physiotherapists, physiatrists and sports trainers for quantitative rehabilitation analysis of the subjects in conjunction with the existing recovery monitoring systems. Based on the results achieved, a conceptual design and model for next generation smart healthcare system/devices for professional athletes has been proposed.
Current Directions in Biomedical Engineering
Patients often report an effect after surgery of the anterior cruciate ligament which is called "giving way". This manifest itself by a drop of the knee or a felt instability. This phenomenon is difficult to measure and validate because it usually does not occur regularly and is not reproducible under laboratory conditions. The Knetex project takes up this point by trying to actively support the rehabilitation process with a bandage that can be worn in everyday life and is constructed as a smart textile using sensors and actuators. For this purpose, on the one hand it is attempted to actively record the phenomenon of the "giving way" by measuring knee angles etc. and by active user feedback. At the same time, the patient is specifically advised by means of actuators to correct incorrect posture or movement in order to make the rehabilitation process more effective and prevent further damage. Two 9-axis IMUs (inertial measurement units) form the basis of the syste...