Assessing post-anterior cruciate ligament reconstruction ambulation using wireless wearable integrated sensors (original) (raw)
Related papers
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.
—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.
MDPI, 2019
In this paper, a gait patterns classification system is proposed, which is based on Mahalanobis-Taguchi System (MTS). The classification of gait patterns is necessary in order to ascertain the rehab outcome among anterior cruciate ligament reconstruction (ACLR) patients. (1) Background: One of the most critical discussion about when ACLR patients should return to work (RTW). The objective was to use Mahalanobis distance (MD) to classify between the gait patterns of the control and ACLR groups, while the Taguchi Method (TM) was employed to choose the useful features. Moreover, MD was also utilised to ascertain whether the ACLR group approaching RTW. The combination of these two methods is called as Mahalanobis-Taguchi System (MTS). (2) Methods: This study compared the gait of 15 control subjects to a group of 10 subjects with laboratory. Later, the data were analysed using MTS. The analysis was based on 11 spatiotemporal parameters. (3) Results: The results showed that gait deviations can be identified successfully, while the ACLR can be classified with higher precision by MTS. The MDs of the healthy group ranged from 0.560 to 1.180, while the MDs of the ACLR group ranged from 2.308 to 1509.811. Out of the 11 spatiotemporal parameters analysed, only eight parameters were considered as useful features. (4) Conclusions: These results indicate that MTS can effectively detect the ACLR recovery progress with reduced number of useful features. MTS enabled doctors or physiotherapists to provide a clinical assessment of their patients with more objective way.
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 ...
International Journal of Electrical and Computer Engineering (IJECE), 2024
This work introduces a statistical analysis of knee range of motion (ROM) and surface electromyography (EMG) data gathered from a knee extension rehabilitation device. Real-time ROM and EMG signals of rehabilitation users are measured using a single angle sensor and a two-channel EMG device (for the vastus lateralis and vastus medialis muscles). These signals are collected by the NI-myRIO embedded device in accordance with the designed rehabilitation program. The main contribution and novelty of this study is that real-time signals are automatically processed and transformed into statistical data for use by users and medical experts. A solution for extracting raw signals is proposed, in which several statistical functions such as range, mean, standard deviation, skewness, percentiles, interquartile range, and total knee holding times above the threshold level, are implemented and applied. The proposed solution is tested using data acquired from healthy people, which includes gender, age, body size, knee side, exercise behavior, and surgical experience. Results indicated that realtime signals and related statistical data on the knee's performance can be efficiently monitored. With this solution, rehabilitation users can practice and learn about their knee performance, while medical experts can evaluate the data and design the best rehabilitation program for users.