Title:
TENSE Ball: A Smart Therapeutic Squeeze Ball to Monitor Hand Activities and Patients’ Pain and Stress Level
TENSE Ball: A Smart Therapeutic Squeeze Ball to Monitor Hand Activities and Patients’ Pain and Stress Level
Author(s)
Shahmiri, Fereshteh
Schwartz, Steven
Usanmaz, Can
Inan, Omer T.
Edwards, W. Keith
Schwartz, Steven
Usanmaz, Can
Inan, Omer T.
Edwards, W. Keith
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Abstract
Hand dexterity, grip strength, and fine motor control are important to our daily routines but can be severely impacted by the development of prognoses such as Parkinson’s disease, Arthritis, recoveries after stroke, surgery, or coma. Simple yet effective squeeze ball exercises have been shown to accelerate recovery, restore mobility, and reduce pain. Hence, there is a need to monitor patient performance and compliance with respect to these exercises along with additional assessments of pain and stress levels.
Commercially available hand exercising balls do not provide quantitative data, which is crucial for assessing patient performance and pain level as well as informing clinicians for proper treatment. To the best of our knowledge, there is no single device in the market or existing research domains, with a flexible form factor that can identify the gripping patterns and correctness of the performed therapeutic exercises as well as assess the pain level that patient is experiencing before and after those exercises. Hence, we designed TENSE ball, a triboelectric nanogenerator-based squeezable electronic ball that is computationally capable of addressing discussed problems, while maintaining the innate features as its non-computational counterparts.
The major contributions of our design include first, capturing psycho-physiological data utilizing an Electro-dermal Activity (EDA) sensor. Second, it monitors the biomechanical status of hand motions and dexterity to assess the correctness of the patient’s performed therapeutic exercises and tracks compliance and improvement with time. Third, it detects hand tremors and other involuntary motion artifacts which allows for the capturing of valuable patient prognostic information.
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Date Issued
2021-03
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Text
Resource Subtype
Poster