Proceedings ArticleDOI
Detection of bouncing during sit-to-stand transfers with sequential pressure images
A. Arcelus,Rafik Goubran,Frank Knoefel,Heidi Sveistrup,Martin Bilodeau +4 more
- pp 158-161
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TLDR
Two algorithms designed for the detection of bouncing from pressure images are presented and one is successful at bounce detection regardless of the region definition, but can lose accuracy when the patient pushes off the bed with little forward lean.Abstract:
As adults age or suffer from physical ailments, their level of functional strength and mobility can decrease over time. One indicator of a loss of muscle strength is the occurrence of bouncing during the sit-to-stand sequence while exiting a bed. This paper presents two algorithms designed for the detection of bouncing from pressure images and tests them on segments of healthy, simulated bouncing and post-stroke patients. The first algorithm relies on pressure measurements from the regions of contact by the hips and hands. This algorithm is found to be most accurate when the hands are both placed on the bed at a distance from the hips but decreases in accuracy when they are either placed too close, on top of the thighs or not used at all. To compensate for this, the second algorithm considers the full image as one region and measures the centroid along with the total pressure over time. This algorithm is successful at bounce detection regardless of the region definition, but can lose accuracy when the patient pushes off the bed with little forward lean. The intelligent fusion of these two algorithms within a mobility monitoring system can provide the detection of bouncing in a wide range of occupants within the smart home environment.read more
Citations
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Proceedings ArticleDOI
Lying and sitting posture recognition and transition detection using a pressure sensor array
TL;DR: This paper demonstrates the use of a bed-based optical pressure sensor array to unobtrusively recognize sitting and lying postures as well as lie-to-sit postural transitions.
Journal ArticleDOI
In-Bed Mobility Monitoring Using Pressure Sensors
TL;DR: The results suggest that, with more development, the presented algorithmic methods could be implemented in hospital settings to assist with identification and assessment of elderly patient mobility.
Journal ArticleDOI
Sensor Fusion Used in Applications for Hand Rehabilitation: A Systematic Review
Irving Herrera-Luna,Ericka Janet Rechy-Ramirez,Homero Vladimir Rios-Figueroa,Antonio Marin-Hernandez +3 more
TL;DR: This systematic review is focused on presenting the state-of-the-art regarding sensor fusion used in the applications for hand rehabilitation, and the most widely used sensor was electromyography sensor followed by the inertial sensors.
Proceedings ArticleDOI
Analyzing center of pressure progression during bed exits.
Zhaofen Ren,Theresa L Grant,Rafik Goubran,Mohamed El-Tanany,Frank Knoefel,Heidi Sveistrup,Martin Bilodeau,Jeffrey W. Jutai +7 more
TL;DR: A new approach for analyzing center of pressure (COP) progression using pressure data collected from a pressure-sensitive array placed under the bed mattress shows the potential to detect changes in bed exit patterns indicative of a critical health event.
Proceedings ArticleDOI
Pressure signal feature extraction for the differentiation of clinical mobility assessments
TL;DR: Results indicate that the movement specific pressure signal features identified here are quantifiable and that algorithms may be written to identify and distinguish between certain movements and output the correct clinical assessment.
References
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Journal ArticleDOI
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Journal ArticleDOI
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