Machine Learning Prediction of Shoulder Patient At-Home Physiotherapy With Inertial Sensors

Because of population ageing, fall prevention represents a human, economic, and social issue. Currently, fall-risk is assessed infrequently, and usually only after the first fall occurrence. Home monitoring could improve fall prevention. Our aim was to monitor daily activities at home in order to identify the behavioral parameters that best discriminate high fall risk from low fall risk individuals. Microsoft Kinect sensors were placed in the room of 30 patients temporarily residing in a rehabilitation center. The sensors captured the patients’ movements while they were going about their daily activities. Different behavioral parameters, such as speed to sit down, gait speed or total sitting time were extracted and analyzed combining statistical and machine learning algorithms. Our algorithms classified the patients according to their estimated fall risk. The automatic fall risk assessment performed by the algorithms was then benchmarked against fall risk assessments performed by clinicians using the Tinetti test and the Timed Up and Go test. Step length, sit-stand transition and total sitting time were the most discriminant parameters to classify patients according to their fall risk. Coupling step length to the speed required to stand up or the total sitting time gave rise to an error-less classification of the patients, i.e., to the same classification as that of the clinicians. A monitoring system extracting step length and sit-stand transitions at home could complement the clinicians’ assessment toolkit and improve fall prevention.

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Network Centric Therapy for Machine Learning Classification of Hemiplegic Gait through Conformal Wearable and Wireless Inertial Sensors

2020 International Conference on e-Health and Bioengineering (EHB) ◽

10.1109/ehb50910.2020.9280189 ◽

2020 ◽

Author(s):

Robert LeMoyne ◽

Timothy Mastroianni

Keyword(s):

Machine Learning ◽

Inertial Sensors ◽

Machine Learning Classification ◽

Hemiplegic Gait

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Wearable Sensors Integrated with Virtual Reality: A Self-Guided Healthcare System Measuring Shoulder Joint Mobility for Frozen Shoulder

Journal of Healthcare Engineering ◽

10.1155/2019/7681237 ◽

2019 ◽

Vol 2019 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Jianjun Cui ◽

Shih-Ching Yeh ◽

Si-Huei Lee

Keyword(s):

Virtual Reality ◽

Shoulder Joint ◽

Inertial Sensors ◽

Wearable Sensors ◽

Frozen Shoulder ◽

Joint Mobility ◽

Joint Movement ◽

Rehabilitation Process ◽

Shoulder Condition ◽

At Home

Frozen shoulder is a common clinical shoulder condition. Measuring the degree of shoulder joint movement is crucial to the rehabilitation process. Such measurements can be used to evaluate the severity of patients’ condition, establish rehabilitation goals and appropriate activity difficulty levels, and understand the effects of rehabilitation. Currently, measurements of the shoulder joint movement degree are typically conducted by therapists using a protractor. However, along with the growth of telerehabilitation, measuring the shoulder joint mobility on patients’ own at home will be needed. In this study, wireless inertial sensors were combined with the virtual reality interactive technology to provide an innovative shoulder joint mobility self-measurement system that can enable patients to measure their performance of four shoulder joint movements on their own at home. Pilot clinical trials were conducted with 25 patients to confirm the feasibility of the system. In addition, the results of correlation and differential analyses compared with the results of traditional measurement methods exhibited a high correlation, verifying the accuracy of the proposed system. Moreover, according to interviews with patients, they are confident in their ability to measure shoulder joint mobility themselves.

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Rehabilitation Exergames: Use of Motion Sensing and Machine Learning to Quantify Exercise Performance in Healthy Volunteers

JMIR Rehabilitation and Assistive Technologies ◽

10.2196/17289 ◽

2020 ◽

Vol 7 (2) ◽

pp. e17289

Author(s):

Reza Haghighi Osgouei ◽

David Soulsby ◽

Fernando Bello

Keyword(s):

Machine Learning ◽

Exercise Program ◽

Machine Learning Algorithms ◽

Qualitative Assessment ◽

Shoulder Abduction ◽

General View ◽

Skeleton Model ◽

Movement Data ◽

Hip Abduction ◽

At Home

Background Performing physiotherapy exercises in front of a physiotherapist yields qualitative assessment notes and immediate feedback. However, practicing the exercises at home lacks feedback on how well patients are performing the prescribed tasks. The absence of proper feedback might result in patients performing the exercises incorrectly, which could worsen their condition. We present an approach to generate performance scores to enable tracking the progress by both the patient at home and the physiotherapist in the clinic. Objective This study aims to propose the use of 2 machine learning algorithms, dynamic time warping (DTW) and hidden Markov model (HMM), to quantitatively assess the patient’s performance with respect to a reference. Methods Movement data were recorded using a motion sensor (Kinect V2), capable of detecting 25 joints in the human skeleton model, and were compared with those of a reference. A total of 16 participants were recruited to perform 4 different exercises: shoulder abduction, hip abduction, lunge, and sit-to-stand exercises. Their performance was compared with that of a physiotherapist as a reference. Results Both algorithms showed a similar trend in assessing participant performance. However, their sensitivity levels were different. Although DTW was more sensitive to small changes, HMM captured a general view of the performance, being less sensitive to the details. Conclusions The chosen algorithms demonstrated their capacity to objectively assess the performance of physical therapy. HMM may be more suitable in the early stages of a physiotherapy program to capture and report general performance, whereas DTW could be used later to focus on the details. The scores enable the patient to monitor their daily performance. They can also be reported back to the physiotherapist to track and assess patient progress, provide feedback, and adjust the exercise program if needed.

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Artificially Intelligent Physiotherapy

International Journal of Biomedical and Clinical Engineering ◽

10.4018/ijbce.2021010106 ◽

2021 ◽

Vol 10 (1) ◽

pp. 77-88

Author(s):

Sachin Pandurang Godse ◽

Shalini Singh ◽

Sonal Khule ◽

Shubham Chandrakant Wakhare ◽

Vedant Yadav

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Range Of Motion ◽

Real Time ◽

Severe Pain ◽

User Performance ◽

Computer Aided ◽

To Come ◽

And Performance ◽

At Home

Physiotherapy is the trending medication for curing bone-related injuries and pain. In many cases, due to sudden jerks or accidents, the patient might suffer from severe pain. Therefore, it is the miracle medication for curing patients. The aim here is to build a framework using artificial intelligence and machine learning for providing patients with a digitalized system for physiotherapy. Even though various computer-aided assessment of physiotherapy rehabilitation exist, recent approaches for computer-aided monitoring and performance lack versatility and robustness. In the authors' approach is to come up with proposition of an application which will record patient physiotherapy exercises and also provide personalized advice based on user performance for refinement of therapy. By using OpenPose Library, the system will detect angle between the joints, and depending upon the range of motion, it will guide patients in accomplishing physiotherapy at home. It will also suggest to patients different physio-exercises. With the help of OpenPose, it is possible to render patient images or real-time video.

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Machine Learning-Based Pre-impact Fall Detection and Injury Prevention for the Elderly with Wearable Inertial Sensors

Lecture Notes in Networks and Systems - Advances in Physical, Social & Occupational Ergonomics ◽

10.1007/978-3-030-80713-9_36 ◽

2021 ◽

pp. 278-285

Author(s):

Xiaoqun Yu ◽

Jaehyuk Jang ◽

Shuping Xiong

Keyword(s):

Machine Learning ◽

Injury Prevention ◽

Inertial Sensors ◽

Fall Detection ◽

The Elderly ◽

Wearable Inertial Sensors

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Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

Electronics ◽

10.3390/electronics9050778 ◽

2020 ◽

Vol 9 (5) ◽

pp. 778

Author(s):

Vasco Ponciano ◽

Ivan Miguel Pires ◽

Fernando Reinaldo Ribeiro ◽

Gonçalo Marques ◽

Maria Vanessa Villasana ◽

...

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Inertial Sensors ◽

Automatic Recognition ◽

Learning Methods ◽

Less Invasive ◽

Machine Learning Methods ◽

The Future

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future.

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