Development of a participation assessment system based on multimodal evaluation of user responses for upper limb rehabilitation

Ödemiş, Erkan; Baysal, Cabbar Veysel

doi:10.1016/j.bspc.2021.103066

Cited by 5 publications

(8 citation statements)

References 41 publications

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“…To overcome the disadvantages of the existing studies mentioned above, in our previous work [ 37 ], we proposed a new participation assessment system that evaluates the therapy performance of patients and other factors affecting the therapy participation, such as tiredness and slacking, independently from any rehabilitation device design or therapy task. The designed system evaluates the patient’s changing capabilities and therapy participation using physiological responses and trajectory-tracking error signals.…”

Section: Introductionmentioning

confidence: 99%

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Clinical evaluation of a patient participation assessment system for upper extremity rehabilitation exercises

Ödemiş,

Baysal

2024

Med Biol Eng Comput

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In conventional and robotic rehabilitation, the patient’s active participation in exercises is essential for the maximum functional output to be received from therapy. In rehabilitation exercises performed with robotic devices, the difficulty levels of therapy tasks and the device assistance are adjusted based on the patient’s therapy performance to improve active participation. However, the existing therapy performance evaluation methods are based on either some specific device designs or certain therapy tasks, which limits their widespread use. In this paper, the effectiveness of a participation assessment system, which can evaluate patients’ therapy performance, tiredness, and slacking independent of any device design and therapy exercise, was clinically tested on ten patients diagnosed with frozen shoulder syndrome. The patients performed exercises using the system once a week throughout their 4-week treatment period. Multiple clinical measurements and scales were employed during the clinical study to assess patients’ progress and status, such as tiredness throughout the therapy process. The clinical data, along with the patient findings obtained from the participation assessment system, were statistically analyzed and compared. The findings revealed that the patients’ improvements and progress during the therapy process clinically coincide with the variations in the performance evaluation results of the system, and the implemented method successfully assesses the patients’ participation during the rehabilitation exercises. Graphical Abstract

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Section: Introductionmentioning

confidence: 99%

“…Also, the developed method adjusts the therapy tasks and task difficulty level according to the subject’s performance and tiredness. The effectiveness of this system was demonstrated experimentally with healthy subjects on five different therapy tasks [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

Clinical evaluation of a patient participation assessment system for upper extremity rehabilitation exercises

Ödemiş,

Baysal

2024

Med Biol Eng Comput

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“…Indeed, telerehabilitation necessitates, more than face-to-face, the use of instruments to assess the therapy and gather indirect information. Quantitative numerical data enables the therapists to track the the rehabilitation progress of the patient, tune the treatment plan as appropriate [3], and share reports with other therapists for different opinions, analytically. For this reason, both kinds of rehabilitation could require quantitative data that, recently, has gained consensus, being its results effectively controllable through objective numbers, allowing the creation of patient history, comparing data from different devices, performing temporal therapy evaluation, etc.…”

Section: Introductionmentioning

confidence: 99%

A DICOM-based Standard for Quantitative Physical Rehabilitation

Matteo,

Lozzi,

Mignosi

et al. 2023

Preprint

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Physical rehabilitation (PR) is crucial in medicine that, yet to date, often relies on qualitative rather than objectively quantifiable data. With the recent scientific and technological advances, several instruments and methods have been proposed aimed at measuring and evaluating rehabilitation objectively, for quantitative PR (QPR), though without a standardized data format. The lack of a standard data format implies several issues, including the inability to compare datafrom different devices, create patient history, perform temporal evaluation or inter-patient comparisons, share data, create and use common evaluation scalesfor therapists, calculate statistics, etc. This lack is also an obstacle to the diffusion of artificial intelligence in rehabilitation, due to the data inhomogeneity,increasing uncertainty, and subjectivity. Digital Imaging and COmmunicationsin Medicine (DICOM) is an affirmed standard, born for medical imaging but recently extended to other fields of medicine, with the primary scope to share data from different devices, handle different associated processes, and assure interoperability among systems and specialists, by generating structured data and documents. In this article, a DICOM Information Object Definition (IOD) is proposed for QPR data. Furthermore, as an example, the proposed DICOM-like standard is applied for managing data from QPR of the hand, generated by the Virtual Glove (VG) device, a multi-source hand-tracking device, and results are discussed.

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“…Using this type of sensor, one of the challenges can be to transform the data from the accelerometer, magnetometer and gyroscope sensor into the orientation of real 3D space. Orientation is a difficult problem to solve, and sensor fusion algorithms that combine accelerometer, magnetometer and gyroscope data into a stable three-axis orientation output can be extremely difficult to correct and implement in low-cost real-time systems [22]. Bosch is the first company to achieve this, taking a MEMS accelerometer, magnetometer and gyroscope and putting them on a single matrix with a high-speed ARM Cortex-M0 processor to process all sensor data, abstract sensor fusion and requirements in real time and obtain data that we can use in quaternions, Euler angles or vectors.…”

Section: Introduction To Mems Sensors That Are Used In the Applicationmentioning

confidence: 99%

“…Micromachines 2021, 12, x FOR PEER REVIEW 4 of 19 correct and implement in low-cost real-time systems [22]. Bosch is the first company to achieve this, taking a MEMS accelerometer, magnetometer and gyroscope and putting them on a single matrix with a high-speed ARM Cortex-M0 processor to process all sensor data, abstract sensor fusion and requirements in real time and obtain data that we can use in quaternions, Euler angles or vectors.…”

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confidence: 99%

Control of a Drone in Virtual Reality Using MEMS Sensor Technology and Machine Learning

Covaciu

Iordan

2022

Micromachines

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In recent years, drones have been widely used in various applications, from entertainment, agriculture, their use in photo and video services, military applications and so on. The risk of accidents while using a drone is quite high. To meet this risk, the most important solution is to use a device that helps and simplifies the control of a drone; in addition, the training of drone pilots is very important. To train the drone pilots, both physical and virtual environments can be used, but the probability of an accident is higher for beginners, so the safest method is to train in a virtual environment. The aim of this study is to develop a new device for controlling a drone in a virtual environment. This device is attached to the upper limb of the person involved in the control of that drone. For precise control, the newly created device uses MEMS sensor technology and artificial intelligence-specific methods.

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Development of a participation assessment system based on multimodal evaluation of user responses for upper limb rehabilitation

Cited by 5 publications

References 41 publications

Clinical evaluation of a patient participation assessment system for upper extremity rehabilitation exercises

Clinical evaluation of a patient participation assessment system for upper extremity rehabilitation exercises

A DICOM-based Standard for Quantitative Physical Rehabilitation

Control of a Drone in Virtual Reality Using MEMS Sensor Technology and Machine Learning

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