Automated Estimation of 3D Camera Extrinsic Parameters for the Monitoring of Physical Activity of Elderly Patients

Deklerck, Rudi; Jansen, Bart; Xiao, Yao; Cornelis, Jan

doi:10.1007/978-3-642-13039-7_176

Cited by 1 publication

(1 citation statement)

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“…The results include simulations of common daily life activities such as walking in different directions, lying on the floor, picking objects from the floor, sitting on the floor or chair and fall events by two healthy adults in the lab. It is also to be noted that it is not feasible to perform actual measurements of key joints in the home environment [ 37 ]; however, the results showed good performance because the proposed algorithm does not require very accurate changes. Rather, it just needs to know rapid changes or the drop of a key joint to floor level.…”

Section: Resultsmentioning

confidence: 99%

Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor

Nizam

Mohd

Jamil

2018

Sensors

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Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection.

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Section: Resultsmentioning

confidence: 99%