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AI in Rehabilitation Medicine: Opportunities and Challenges
Francesco Lanotte, Megan K. O’Brien, Arun Jayaraman
Ann Rehabil Med 2023;47(6):444-458.   Published online December 14, 2023
DOI: https://doi.org/10.5535/arm.23131
Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex data, thus allowing clinicians and scientists to gain new insights from the information they collect about their patients every day. In rehabilitation medicine, AI can be used to find patterns in huge amounts of healthcare data. These patterns can then be leveraged at the individual level, to design personalized care strategies and interventions to optimize each patient’s outcomes. However, building effective AI tools requires many careful considerations about how we collect and handle data, how we train the models, and how we interpret results. In this perspective, we discuss some of the current opportunities and challenges for AI in rehabilitation. We first review recent trends in AI for the screening, diagnosis, treatment, and continuous monitoring of disease or injury, with a special focus on the different types of healthcare data used for these applications. We then examine potential barriers to designing and integrating AI into the clinical workflow, and we propose an end-to-end framework to address these barriers and guide the development of effective AI for rehabilitation. Finally, we present ideas for future work to pave the way for AI implementation in real-world rehabilitation practices.

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Original Article
Mobile Sensor Application for Kinematic Detection of the Knees
Tossaphon Jaysrichai, Areerat Suputtitada, Watcharapong Khovidhungij
Ann Rehabil Med 2015;39(4):599-608.   Published online August 25, 2015
DOI: https://doi.org/10.5535/arm.2015.39.4.599
Objective

To correctly measure the knee joint angle, this study utilized a Qualisys motion capture system and also used it as the reference to assess the validity of the study's Inertial Measurement Unit (IMU) system that consisted of four IMU sensors and the Knee Angle Recorder software. The validity was evaluated by the root mean square (RMS) of different angles and the intraclass correlation coefficient (ICC) values between the Qualisys system and the IMU system.

Methods

Four functional knee movement tests for ten healthy participants were investigated, which were the knee flexion test, the hip and knee flexion test, the forward step test and the leg abduction test, and the walking test.

Results

The outcomes of the knee flexion test, the hip and knee flexion test, the forward step test, and the walking test showed that the RMS of different angles were less than 6°. The ICC values were in the range of 0.84 to 0.99. However, the leg abduction test showed a poor correlation in the measurement of the knee abduction-adduction movement.

Conclusion

The IMU system used in this study is a new good method to measure the knee flexion-extension movement.

Citations

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