Real-Time Limb Motion Tracking with a Single IMU Sensor for Physical Therapy Exercises (original) (raw)
Related papers
A Limb Tracking Platform for Tele-Rehabilitation
ACM Transactions on Cyber-Physical Systems, 2018
The adoption of motor-rehabilitative therapies is highly demanded in a society where the average age of the population is constantly increasing. A recent trend to contain costs while providing high quality of healthcare services is to foster the adoption of self-care procedures, performed primarily in patients’ environments rather than in hospitals or healthcare structures, especially in the case of intensive and chronic patients’ rehabilitation. This work presents a platform to enhance limb functional recovery through telerehabilitation sessions. It relies on a sensing system based on inertial sensors and data fusion algorithms, a module to provide bio-feedback tailored to the users, and a module dedicated to the physicians’ practices. The system design had to face several cyber-physical challenges due to the tight interaction between patient and sensors. For instance, integrating the body kinematics into the sensory processing improved the precision of measurements, simplified the...
A database of physical therapy exercises with variability of execution collected by wearable sensors
Scientific Data, 2022
a database of physical therapy exercises with variability of execution collected by wearable sensors Sara García-de-Villa ✉ , ana Jiménez-Martín & Juan Jesús García-Domínguez this document introduces the PHYtMO database, which contains data from physical therapies recorded with inertial sensors, including information from an optical reference system. PHYtMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. PHYTMO includes magneto-inertial data, together with a highly accurate location and orientation in the 3D space provided by the optical system. The files were stored in CSV format to ensure its usability. the aim of this dataset is the availability of data for two main purposes: the analysis of techniques for the identification and evaluation of exercises using inertial sensors and the validation of inertial sensor-based algorithms for human motion monitoring. Furthermore, the database stores enough data to apply Machine Learning-based algorithms. the participants' age range is large enough to establish age-based metrics for the exercises evaluation or the study of differences in motions between different groups.
A novel motion tracking system for evaluation of functional rehabilitation of the upper limbs
Neural regeneration research, 2013
Upper limb function impairment is one of the most common sequelae of central nervous system injury, especially in stroke patients and when spinal cord injury produces tetraplegia. Conventional assessment methods cannot provide objective evaluation of patient performance and the tiveness of therapies. The most common assessment tools are based on rating scales, which are inefficient when measuring small changes and can yield subjective bias. In this study, we designed an inertial sensor-based monitoring system composed of five sensors to measure and analyze the complex movements of the upper limbs, which are common in activities of daily living. We developed a kinematic model with nine degrees of freedom to analyze upper limb and head movements in three dimensions. This system was then validated using a commercial optoelectronic system. These findings suggest that an inertial sensor-based motion tracking system can be used in patients who have upper limb impairment through data integ...
The limb movement analysis of rehabilitation exercises using wearable inertial sensors
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016
Due to no supervision of a therapist in home based exercise programs, inertial sensor based feedback systems which can accurately assess movement repetitions are urgently required. The synchronicity and the degrees of freedom both show that one movement might resemble another movement signal which is mixed in with another not precisely defined movement. Therefore, the data and feature selections are important for movement analysis. This paper explores the data and feature selection for the limb movement analysis of rehabilitation exercises. The results highlight that the classification accuracy is very sensitive to the mount location of the sensors. The results show that the use of 2 or 3 sensor units, the combination of acceleration and gyroscope data, and the feature sets combined by the statistical feature set with another type of feature, can significantly improve the classification accuracy rates. The results illustrate that acceleration data is more effective than gyroscope data for most of the movement analysis.
Fully portable low-cost motion capture system with real-time feedback for rehabilitation treatment
2019 International Conference on Virtual Rehabilitation (ICVR), 2019
To date, technologically-based rehabilitation methods have been widely used for treating disabilities. The evolution of new technologies utilizing motion capture systems or wearable tracking sensors have further enhanced the standalone self-treatments which benefit both the patients and the physicians. However, current systems have not yet proved to be truly mobile or low-cost, since they do not only need significant processing power to operate or tech-savvy operators, but they also have patients visiting the clinic regularly, more often than expected for a home-based system, in order to receive feedback on their performance. This study presents and proposes a fully portable, low-cost motion capture system that supervises the progress of patients whilst each exercise is being executed; thereby, it provides physicians a more mathematically precise way of evaluating patients’ performance and progress through reports generated by the mobile application. For the purposes of this study, ...
RehabGesture: An Alternative Tool for Measuring Human Movement
Telemedicine and e-Health, 2016
Background: Systems for range of motion (ROM) measurement such as OptoTrak, Motion Capture, Motion Analysis, Vicon, and Visual 3D are so expensive that they become impracticable in public health systems and even in private rehabilitation clinics. Telerehabilitation is a branch within telemedicine intended to offer ways to increase motor and/or cognitive stimuli, aimed at faster and more effective recovery of given disabilities, and to measure kinematic data such as the improvement in ROM. Materials and Methods: In the development of the RehabGesture tool, we used the gesture recognition sensor Kinect Ò (Microsoft, Redmond, WA) and the concepts of Natural User Interface and Open Natural Interaction. Results: RehabGesture can measure and record the ROM during rehabilitation sessions while the user interacts with the virtual reality environment. The software allows the measurement of the ROM (in the coronal plane) from 0°extension to 145°flexion of the elbow joint, as well as from 0°a dduction to 180°abduction of the glenohumeral (shoulder) joint, leaving the standing position. The proposed tool has application in the fields of training and physical evaluation of professional and amateur athletes in clubs and gyms and may have application in rehabilitation and physiotherapy clinics for patients with compromised motor abilities. Conclusions: RehabGesture represents a low-cost solution to measure the movement of the upper limbs, as well as to stimulate the process of teaching and learning in disciplines related to the study of human movement, such as kinesiology.
Procedia Engineering, 2016
Biomechanical analysis of movement during sport practice is extremely useful to assess and, subsequently, optimise movement performance during sport which can also assist athletes during rehabilitation following injury (such as Anterior Cruciate Ligament reconstruction). It is mostly performed using camera-based motion analysis systems, which provide good results but present serious drawbacks (for instance, consistent size, high cost, and lack of portability). Thus, small-size low-cost wearable sensors are an emerging tool for biomechanics monitoring. Aim of the present work is to implement a novel wireless portable easy-to-use system, consisting of two Tyndall Wireless Inertial Measurement Units (WIMUs) per leg, suitable for free-living environments and able to provide a complete biomechanics assessment (generated on a report) without the constraints of a laboratory. Validation for the lower-limbs using state-of-the-art camera-based motion capture is presented here. Algorithms are implemented in Matlab, and the scenarios considered simulate a free-living environment and exercises performed in a rehabilitation procedure. The system has been validated with healthy and impaired subjects. This novel system shows high accuracy values for all considered scenarios. Moreover, it is able to detect atypical movement characteristics. The results of this feasibility study support the next phase which will be to assess the external and ecological validity of athletes' on-field movement performance, which will help to inform individualised training protocols or enhance targeted rehabilitation programmes.
A wearable sensing system for tracking and monitoring of functional arm movement
2011
Abstract This paper presents a new sensing system for home-based rehabilitation based on optical linear encoder (OLE), in which the motion of an optical encoder on a code strip is converted to the limb joints' goniometric data. A body sensing module was designed, integrating the OLE and an accelerometer. A sensor network of three sensing modules was established via controller area network bus to capture human arm motion.
Real-Time Range of Motion Measurement of Physical Therapeutic Exercises
Journal of Computer and Communications, 2017
Physical therapeutic exercise (PTE) is the planned process of performing bodily movements, postures, or physical activities to provide a patient with the ability to remediate or prevent impairments at a minimum. The efficacy of the PTE involves measuring accurately the range of motion (ROM) of joint functions and parameters that indicate the onset of fatigue, jerky motion, and muscle/joint resistance to the PTE. A physical therapist (PT) typically determines the efficacy of a PTE by measuring joint angles in clinical diagnosis to assess the ROM using the simple device Goniometer since motion capture systems are generally expensive, difficult to use, and currently not suited for real-time operations. The joint angle measurement using Goniometer suffers from low accuracy, low reliability and subjective. Furthermore, a patient when performing PTE by themselves at remote locations like their home or community centers cannot use a Goniometer to determine the efficacy. In this study, we present the approach of using an inexpensive, simple human motion capture system (HMCS) consisting of a single camera and a graphical processing unit (GPU) to perform the efficacy of the PTE in real-time. The approach involves the use of general purpose graphic processing unit (GPGPU) computer vision technique to track and record human motion and relate the tracked human motion to the prescribed physical therapy regimen in real-time. We have developed a tracking algorithm derived from the Klein's algorithm known as the Modified Klein's algorithm (MKA) capable of tracking human body parts while the original Klein's algorithm was only capable of tracking objects with sharp edges. The MKA algorithm is further modified for parallel execution on a GPU to operate in real-time. Using the GPU, we are able to track multiple markers in a high definition (HD) frame of the HD video in 1.77 msecs achieving near real-time capability of ROM measurements.
Wireless Motion Capture System for Upper Limb Rehabilitation
2021
This work is devoted to the presentation of a Wireless Sensor System implementation for upper limb rehabilitation to function as a complementary system for a patient’s progress supervision during rehabilitation exercises. A cost effective motion capture sensor node composed by a 9 Degrees-of-Freedom (DoF) Inertial Measurement Unit (IMU) is mounted on the patient’s upper limb segments and sends wirelessly the corresponding measured signals to a base station. The sensor orientation and the upper limb individual segments movement in 3-Dimensional (3D) space are derived by processing the sensors’ raw data. For the latter purpose, a biomechanical model which resembles that of a kinematic model of a robotic arm based on the Denavit-Hartenberg (DH) configuration is used to approximate in real time the upper limb movements. The joint angles of the upper limb model are estimated from the extracted sensor node’s orientation angles. The experimental results of a human performing common rehabil...