Accelerometers in Rehabilitation
Original Editor - Ananya Bunglae Sudindar
Top Contributors - Ananya Bunglae Sudindar
Introduction[edit | edit source]
Wearable devices, also known as wearables, are instruments that have garnered significant attention for enabling non-invasive, real-time monitoring of physical and physiological parameters in smart care and digital medicine. These devices are worn on the body and provide physiological measures directly to smart devices, offering valuable insights into health metrics. [1][2][3]
Accelerometers are wearable devices designed to measure the acceleration of the body segment to which they are attached[4]. These devices play a crucial role in studying human movement across various applications, including activity detection, assessing postural balance, evaluating sports physical function, and investigating falls. They operate based on Newton’s law of motion wherein they measure linear acceleration which represents the change in an object’s speed over time. [5]
Application in Rehabilitation[edit | edit source]
Gait Analysis[edit | edit source]
Gait analysis using accelerometers often involves placing sensors at various body segments in order to capture the spatiotemporal parameters of an individual's gait pattern. The collected data are then analyzed for clinical applications, disease-specific insights, and rehabilitation monitoring, providing valuable information for healthcare professionals and researchers.
Sensor Placement and Data Collection[edit | edit source]
- Accelerometers are strategically placed on different body parts, such as ears, thighs, pelvis, feet, and others, depending on the specific conditions and objectives of the evaluation.
- Multiple sensors may be used simultaneously to obtain comprehensive data about the entire body's movement.
- The accelerometers record the accelerations and decelerations of body segments during the gait cycle.
- Accelerometers such as triaxial accelerometers capture movement in three dimensions, providing detailed information about motion in the frontal, sagittal, and transverse planes
- The collected data are utilized to assess specific aspects of gait, such as symmetry, balance, stride length, step frequency, and temporal parameters.
Clinical Use[edit | edit source]
- Gait analysis using accelerometers is applied across various clinical conditions, including orthopaedic and neurological conditions.
- Gait analysis is tailored to the characteristics of specific diseases or conditions, such as Parkinson's disease, Huntington's disease, Cerebral Palsy, Orthopaedic injuries, and more.
- For example, in Parkinson's disease, gait analysis may focus on detecting freezing of gait (FoG) and other abnormalities.
- Accelerometers can be used to monitor the rehabilitation progress of patients by tracking the changes in gait patterns over time. It can also be used to assess the effectiveness of rehabilitation programmes.
Monitoring Physical Activity Levels[edit | edit source]
Physical activity (PA) plays a crucial role in maintaining the health and well-being of individuals. In recent years, accelerometers have emerged as essential tools for assessing PA. These devices are integral to clinical practice and contribute significantly to evaluating PA levels and monitoring treatment progress in individuals with chronic diseases. [7][8]
Sensor Placement[edit | edit source]
The choice of accelerometer placement is subjective and depends on various factors such as the targeted activities and the consideration of participant burden. The most common sensor placements to measure physical activity include:
- Wrist: It is the most popular choice of location for placing accelerometers but has accuracy limitations across various activities.
- Hip: Hip placement is considered to be the most reliable location due to its high accuracy while evaluating different kinds of activities.
- Ankle: Placing the sensor at the ankle is often beneficial for faster activities but should be used with caution while evaluating slower activities.
- Thigh: Sensors placed on the thigh show good performance across various activities.
Clinical Use[edit | edit source]
Other Applications[edit | edit source]
- Postural evaluation
- Balance training
- Fall prevention
Advantages of Accelerometers[edit | edit source]
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Challenges of Using Accelerometers in Rehabilitation[edit | edit source]
References[edit | edit source]
- ↑ Miller DJ, Sargent C, Roach GD. A validation of six wearable devices for estimating sleep, heart rate and heart rate variability in healthy adults. Sensors. 2022 Aug 22;22(16):6317.
- ↑ Lu L, Zhang J, Xie Y, Gao F, Xu S, Wu X, Ye Z. Wearable health devices in health care: narrative systematic review. JMIR mHealth and uHealth. 2020 Nov 9;8(11):e18907.
- ↑ Lin WY, Chen CH, Lee MY. Design and implementation of a wearable accelerometer-based motion/tilt sensing internet of things module and its application to bed fall prevention. Biosensors. 2021 Oct 29;11(11):428.
- ↑ Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle Nyström C, Mora-Gonzalez J, Löf M, Labayen I, Ruiz JR, Ortega FB. Accelerometer data collection and processing criteria to assess physical activity and other outcomes: a systematic review and practical considerations. Sports medicine. 2017 Sep;47:1821-45.
- ↑ Celik Y, Vitorio R, Powell D, Moore J, Young F, Coulby G, Tung J, Nouredanesh M, Ellis R, Izmailova ES, Stuart S. Sensor Integration for Gait Analysis.
- ↑ Jarchi D, Pope J, Lee TK, Tamjidi L, Mirzaei A, Sanei S. A review on accelerometry-based gait analysis and emerging clinical applications. IEEE reviews in biomedical engineering. 2018 Feb 16;11:177-94.
- ↑ Allahbakhshi H, Hinrichs T, Huang H, Weibel R. The key factors in physical activity type detection using real-life data: A systematic review. Frontiers in physiology. 2019 Feb 12;10:75
- ↑ Davoudi A, Mardini MT, Nelson D, Albinali F, Ranka S, Rashidi P, Manini TM. The effect of sensor placement and number on physical activity recognition and energy expenditure estimation in older adults: Validation study. JMIR mHealth and uHealth. 2021 May 3;9(5):e23681.
- ↑ Davoudi A, Mardini MT, Nelson D, Albinali F, Ranka S, Rashidi P, Manini TM. The effect of sensor placement and number on physical activity recognition and energy expenditure estimation in older adults: Validation study. JMIR mHealth and uHealth. 2021 May 3;9(5):e23681.
- ↑ Marshall MR, Montoye AH, Conway MR, Schlaff RA, Pfeiffer KA, Pivarnik JM. Location, Location, Location: Accelerometer Placement Affects Steps-Based Physical Activity Outcomes During Pregnancy and Postpartum. American Journal of Lifestyle Medicine. 2023 Jan;17(1):123-30.
