Comparison of low-complexity fall detection algorithms for body attached accelerometers.

By Maarit Kangas, Antti Konttila, P. Lindgren, I. Winblad, T. Jämsä. Gait & Posture, 2008

511 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Physical Therapy, Sports Therapy and Rehabilitation

View Paper: https://www.semanticscholar.org/paper/f0b70ad67e68153714e7150fcd831d4b88697859

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Physical Therapy, Sports Therapy and Rehabilitation

Citations in the corpus

Elderly Fall Detection Systems: A Literature Survey (2020, background): “Although there has been an attempt to use middle-age subjects to simulate falls (Kangas et al., 2008), there are still diﬀerences in behavior between the elderly and middle-aged subjects.”
Challenges, issues and trends in fall detection systems (2013, methodology): “[36] 2008 Comparison of 3 low-complexity algorithms TBM considering the start of the fall, the velocity, the impact and the lying posture Forward, backward, and lateral falls 3 volunteers (1 fem e, 2 males; ages 38, 42 8) Waist Wrist, head, waist No Waist worn accelerometer might be optimal for fall”
Human fall detection on embedded platform using depth maps and wireless accelerometer (2014, methodology): “It is worth noting that the attachment of the wearable sensor near the pelvis region or lower back is recommended in the literature [11] because such a body part represents the major component of body mass and undergoes movement in most activities.”
Gait Analysis Using Wearable Sensors (2012, methodology): “determined the acceleration thresholds for fall detection using tri-axial accelerometers attached at the waist and head, analyzed the sensitivity and specificity of fall detection in people aged 40 years and over, and gave an overall comparison of different fall-detection algorithms [180–182].”
A viewpoint-independent statistical method for fall detection (2012, background): “Several approaches have been proposed for fall detection, some recent reviews include [5, 13].”
Human Activity Recognition Using Inertial/Magnetic Sensor Units (2010, methodology): “Detecting and classifying falls using inertial sensors is another important problem that has not been sufficiently well investigated [10], due to the difficulty of designing and performing fair and realistic experiments in this area [6].”
Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units (2014, background): “Detection of falls during daily activities and their classification has not been sufficiently investigated [73] due to the difficulty of performing realistic experiments in this area [14], especially for involuntary falls.”