Wireless Motion Capture System for Upper Limb Rehabilitation

Tsilomitrou, Ourania; Γκούντας, Κωνσταντίνος; Evangeliou, Nikolaos; Dermatas, Evangelos

doi:10.3390/asi4010014

Cited by 10 publications

(3 citation statements)

References 40 publications

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“…They typically combine accelerometers, gyroscopes, and sometimes magnetometers to measure linear acceleration, angular velocity, and orientation [31]. These sensors are particularly useful for tracking dynamic movements of body segments, providing real-time data on joint angles, velocities, and accelerations [32][33][34]. Figure 5a shows a wearable IMU setup for arm motion acquisition.…”

Section: Motion Acquisitionmentioning

confidence: 99%

Developing a Cyber-Physical Rehabilitation System for Virtual Interaction between Patients and Occupational Therapists

Cheng,

Camargo,

Bakhoum

2024

Preprint

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The integration of robotic devices into healthcare settings for rehabilitation purposes represents a rapidly advancing field with significant benefits for both therapists and patients. However, standalone robot systems face limitations such as inefficient processing of patient data and logistical challenges for patients, such as travel time to rehabilitation centers. To address these shortcomings, this paper proposes the integration of cyber-physical systems into rehabilitation robotics. Cyber-physical systems leverage network connectivity and real-time data processing to enhance the efficiency and convenience of robot-based rehabilitation. By seamlessly integrating sensors, actuators, and computational algorithms, cyber-physical rehabilitation systems empower therapists to more effectively treat patients by providing actionable insights derived from comprehensive data analysis. This enables therapists to tailor treatment plans precisely to each patient’s unique needs, ultimately leading to improved outcomes. Additionally, cyber-physical rehabilitation systems facilitate remote monitoring and intervention, allowing therapists to oversee patient progress and make timely adjustments to treatment protocols without the need for in-person visits. This not only saves patients valuable time and resources but also ensures continuity of care, particularly for individuals residing in remote or underserved areas. Furthermore, cyber-physical rehabilitation systems enable collaborative decision-making by facilitating communication and information sharing among healthcare professionals, further enhancing the quality and efficacy of rehabilitation services. In summary, integrating cyber-physical systems into rehabilitation robotics represents a significant advancement in leveraging technology to optimize patient care and outcomes, promising to revolutionize rehabilitation therapy.

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Section: Motion Acquisitionmentioning

confidence: 99%

Developing a Cyber-Physical Rehabilitation System for Virtual Interaction between Patients and Occupational Therapists

Cheng,

Camargo,

Bakhoum

2024

Preprint

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“…In [54], phone sensors were used; when comparing different approaches, the error was up to 5% when moving over long distances (hundreds of meters), but with small movements, as in our study, it can be higher. High accuracy (up to 0.07 m) when using an IMU can be achieved by combining several devices connected to a single network [55], but such a design could approach motion capture systems in terms of implementation complexity, yielding to them in universality.…”

Section: Comparison Of Motion Tracking Systems In Musculoskeletal Reh...mentioning

confidence: 99%

Examination of the Accuracy of Movement Tracking Systems for Monitoring Exercise for Musculoskeletal Rehabilitation

Obukhov,

Volkov,

Pchelintsev

et al. 2023

Sensors

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When patients perform musculoskeletal rehabilitation exercises, it is of great importance to observe the correctness of their performance. The aim of this study is to increase the accuracy of recognizing human movements during exercise. The process of monitoring and evaluating musculoskeletal rehabilitation exercises was modeled using various tracking systems, and the necessary algorithms for processing information for each of the tracking systems were formalized. An approach to classifying exercises using machine learning methods is presented. Experimental studies were conducted to identify the most accurate tracking systems (virtual reality trackers, motion capture, and computer vision). A comparison of machine learning models is carried out to solve the problem of classifying musculoskeletal rehabilitation exercises, and 96% accuracy is obtained when using multilayer dense neural networks. With the use of computer vision technologies and the processing of a full set of body points, the accuracy of classification achieved is 100%. The hypotheses on the ranking of tracking systems based on the accuracy of positioning of human target points, the presence of restrictions on application in the field of musculoskeletal rehabilitation, and the potential to classify exercises are fully confirmed.

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“…Its main purpose is to record the movements of an object, in this case the human body, for later analysis and to apply data modeling [4,5]. In human motion capture, it is required to establish the position and orientation of each of the body segments [6][7][8]. Technological development has resulted in various systems for measuring the position of body segments and angles, most notably optical and Inertial Measurement Units (IMU).…”

Section: Introductionmentioning

confidence: 99%

Vertical Jump Data from Inertial and Optical Motion Tracking Systems

Rico-García

Botero-Valencia

Hernández-García

2022

Data

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Motion capture (MOCAP) is a widely used technique to record human, animal, and object movement for various applications such as animation, biomechanical assessment, and control systems. Different systems have been proposed based on diverse technologies, such as visible light cameras, infrared cameras with passive or active markers, inertial systems, or goniometer-based systems. Each system has pros and cons that make it usable in different scenarios. This paper presents a dataset that combines Optical Motion and Inertial Systems, capturing a well-known sports movement as the vertical jump. As a reference system, the optical motion capture consists of six Flex 3 Optitrack cameras with 100 FPS. On the other hand, we developed an inertial system consisting of seven custom-made devices based on the IMU MPU-9250, which includes a three-axis magnetometer, accelerometer and gyroscope, and an embedded Digital Motion Processor (DMP) attached to a microcontroller mounted on a Teensy 3.2 with an ARM Cortex-M4 processor with wireless operation using Bluetooth. The purpose of taking IMU data with a low-cost and customized system is the deployment of applications that can be performed with similar hardware and can be adjusted to different areas. The developed measurement system is flexible, and the acquisition format and enclosure can be customized. The proposed dataset comprises eight jumps recorded from four healthy humans using both systems. Experimental results on the dataset show two usage examples for measuring joint angles and COM position. The proposed dataset is publicly available online and can be used in comparative algorithms, biomechanical studies, skeleton reconstruction, sensor fusion techniques, or machine learning models.

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Wireless Motion Capture System for Upper Limb Rehabilitation

Cited by 10 publications

References 40 publications

Developing a Cyber-Physical Rehabilitation System for Virtual Interaction between Patients and Occupational Therapists

Developing a Cyber-Physical Rehabilitation System for Virtual Interaction between Patients and Occupational Therapists

Examination of the Accuracy of Movement Tracking Systems for Monitoring Exercise for Musculoskeletal Rehabilitation

Vertical Jump Data from Inertial and Optical Motion Tracking Systems

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