An accelerometer-based physical rehabilitation system

Milenković, Marina; Jovanov, Emil; Chapman, Judith; Raskovic, Dejan; Price, J.

doi:10.1109/ssst.2002.1027005

Cited by 22 publications

(15 citation statements)

References 4 publications

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“…Many researchers have developed IMU-based systems to support patients with orthopaedic or musculoskeletal conditions as they perform their rehabilitation exercises at home, providing them with real-time feedback [20][21][22][23][24]. However these platforms have only been evaluated in laboratory based studies.…”

Section: Existing State Of the Artmentioning

confidence: 99%

“…The results of this investigation showed that the 2-week interactive rehabilitation programme was as effective as the conventional rehabilitation programme, thus highlighting the promise for a sensor-based rehabilitation support. However it is worth noting that this system, as well as those referenced in [20][21][22][23][24], used multiple sensors to quantify exercise performance. Using multiple sensors is not convenient for patients to set-up and use in the home.…”

Section: Existing State Of the Artmentioning

confidence: 99%

See 1 more Smart Citation

Proposed Design Approach for an Interactive Feedback Technology Support in Rehabilitation

Giggins

Caulfield

2015

Proceedings of the 3rd 2015 Workshop on ICTs for Improving Patients Rehabilitation Research Techniques

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Despite the anticipated benefits associated with the use interactive feedback technology, the adoption of these technologies into clinical practice has not yet been widespread. Initial usability studies and anecdotal evidence suggests that interactive feedback technology supports are effective in rehabilitation, yet they don't seem to satisfy the needs or the expectations of clinicians and patients. This paper outlines a proposed approach to the design, development and selection of interactive feedback supports for use in rehabilitation. The proposed process comprises seven stages; 1) Understand the problem, 2) Identify the benefits that a technology support may offer, 3) Devise a modified care pathway, 4) Outline the design specification for an interactive technology support, 5) Ascertain whether other solutions exist, 6) Develop interactive feedback technology platform, 7) And finally evaluate. In this paper we outline this approach using orthopaedic rehabilitation as a specific example, however the same basic principles can be applied to many different rehabilitation groups.

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Section: Existing State Of the Artmentioning

confidence: 99%

Section: Existing State Of the Artmentioning

confidence: 99%

Proposed Design Approach for an Interactive Feedback Technology Support in Rehabilitation

Giggins

Caulfield

2015

Proceedings of the 3rd 2015 Workshop on ICTs for Improving Patients Rehabilitation Research Techniques

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“…With the increasing ubiquity of mobile sensors (e.g. accelerometers and gyroscopes), many researchers have explored their use in-lab for supporting physical therapy treatment [5,10,11,17,18,19]. These wearable systems range from simple (single sensor) to complex (multiple sensors, body area networks, and so forth).…”

Section: Technological Support For Physical Therapymentioning

confidence: 99%

A technology probe of wearable in-home computer-assisted physical therapy

Huang

Sparto

Kiesler

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Physical therapists could make better treatment decisions if they had accurate patient home exercise data but today this information is only available from patient self-report. A more accurate source of data could be gained from wearable computing designed for physical therapy exercise support. Existing systems have been tested in the lab but we have little information about issues they may face in home settings. We designed a technology probe, SenseCap, and deployed it for seven days in ten physical therapy patients' homes. SenseCap is a wearable physical therapy support system that gathers patient exercise compliance and performance data and summarizes the data in charts on an iPad Dashboard for physical therapists to view when patients return to the clinic. In this paper, we present the results of our deployment, show in-home patient exercise data gathered by the probe, and make design recommendations based on patient and physical therapist responses.

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“…However, many of the earlier studies are based on estimating the activity/therapy intensity [1,6,10] or the energy expenditure [8] using the sensors rather than determining the accuracy of the physical therapy exercises. In numerous studies, a 3-D real-time human body model is constructed to observe the movements [1,2,4,11,12,18,19].…”

Section: Related Workmentioning

confidence: 99%

Detection and Evaluation of Physical Therapy Exercises by Dynamic Time Warping Using Wearable Motion Sensor Units

Yurtman

Barshan

2013

Information Sciences and Systems 2013

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We develop an autonomous system that detects and evaluates physical therapy exercises using wearable motion sensors. We propose an algorithm that detects all the occurrences of one or more template signals (representing exercise movements) in a long signal acquired during a physical therapy session. In matching the signals, the algorithm allows some distortion in time, based on dynamic time warping (DTW). The algorithm classifies the executions in one of the exercises and evaluates them as correct/incorrect, giving the error type if there is any. It also provides a quantitative measure of similarity between each matched execution and its template. To evaluate the performance of the algorithm in physical therapy, a dataset consisting of one template execution and ten test executions of each of the three execution types of eight exercises performed by five subjects is recorded, having a total of 120 and 1,200 exercise executions in the training and test sets, respectively, as well as many idle time intervals in the test signals. The proposed algorithm detects 1,125 executions in the whole test set. 8.58 % of the 1,200 executions are missed and 4.91 % of the idle time intervals are incorrectly detected as executions. The accuracy is 93.46 % only for exercise classification and 88.65 % for simultaneous exercise and execution type classification. The proposed system may be used for both estimating the intensity of the physical therapy session and evaluating the executions to provide feedback to the patient and the specialist.

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An accelerometer-based physical rehabilitation system

Cited by 22 publications

References 4 publications

Proposed Design Approach for an Interactive Feedback Technology Support in Rehabilitation

Proposed Design Approach for an Interactive Feedback Technology Support in Rehabilitation

A technology probe of wearable in-home computer-assisted physical therapy

Detection and Evaluation of Physical Therapy Exercises by Dynamic Time Warping Using Wearable Motion Sensor Units

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