Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly

By Bijan Najafi, Kamiar Aminian, François Loew, Yves Blanc, Philippe A. Robert. IEEE Transactions on Biomedical Engineering, 2002

435 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Orthopedics and Sports Medicine, Physical Therapy, Sports Therapy and Rehabilitation

View Paper: https://www.semanticscholar.org/paper/9e6603a8ee02d03603932e859bb01b9077240760

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In Physical Therapy, Sports Therapy and Rehabilitation

Citations in the corpus

Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly (2003, methodology): “The minimum peak of was considered as the time of PT, which its duration ( TD) was determined from as described in our previous publications [23], [24].”
Comparative study on classifying human activities with miniature inertial and magnetic sensors (2010, methodology): “The five MTx units are connected with 1 m cables to a device called the Xbus Master, which is attached to the subject’s belt.”
Activity identification using body-mounted sensors—a review of classification techniques (2009, methodology): “Threshold-based classification has also been used successfully to identify postural transitions using data on the change in segmental angles derived from either accelerometers (Najafi et al 2003) or gyroscopes (Najafi et al 2002; Najafi et al 2003).”
Human motion tracking for rehabilitation - A survey (2008, background): “proposed to use a miniature gyroscope to conduct a study on the falls of the elderly [114].”
Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units (2014, background): “In a number of studies, video cameras are used only as a reference for comparison with inertial sensor data [20] [21] [22] [23], whereas in others, data from these two sensing modalities are integrated or fused [24] [25].”
The relevance of clinical balance assessment tools to differentiate balance deficits (2010, positive): “, 2001) or to determine time spent in various activities such as lying down, walking, sitting, and standing (Tanaka, et al, 1994; Najafi et al., 2002; Najafi et al., 2003).”
Physical Human Activity Recognition Using Wearable Sensors (2015, methodology): “Postural transitions have been also classified using conventional threshold-based algorithms [13,84].”
A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data (2009, background): “Ambulatory monitoring of various activities, including the time spent in sit-stand transitions have shown promise as predictors of fall-risk [8].”