2008 · 18 citations · 16 references
EducationPhysical MedicineAssistive TechnologyFunctional Movement AssessmentMovement AnalysisKinematic AnalysisMobility AssessmentMotion CaptureMotion SensorsMovement KinematicsWearable Health TechnologiesActivitiesActivity MonitoringKinematic MeasurementKinematic SensingMotion SensingWearable Motion SensorsWearable Sensor-based AssessmentWireless Biomedical Sensors
The objective of this on-going work is to evaluate the accuracy and reliability of wireless kinematic sensors in identifying basic Activities of Daily Living (ADL). A preliminary trial was conducted consisting of 5 subjects; 3 male (mean: 23.6, SD: 2.41). Four kinematic sensors were placed on a subject; (a) mid-sternum, (b) underneath the left armpit, (c) above the right hip and (d) the ankle of the dominant leg. A fifth sensor, the activPAL Trio was attached to a subject on the thigh of the non-dominant leg. Each subject initially performed a range of basic activities in a controlled laboratory setting. Subjects were then asked to carry out similar self-annotated activities in a random order and in an unsupervised environment.Using 10-fold cross validation the decision tree algorithm C4.5 was employed to detect the ADL's. Several configurations of sensor placement were compared. The combination of sensors placed on the ankle and hip had the highest recognition rate of 81.2%. Single sensor placement was also compared with the ankle of the dominant leg having the highest recognition rate of 77.4%.
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Ian H. Witten, Eibe Frank · ACM SIGMOD Record · 2002 · 5.2K citations
Activity recognition from accelerometer data
Nishkam Ravi, Nikhil Dandekar, Preetham Mysore et al. · 2005 · 1.4K citations
Computing, Data Science, Data Mining +16
Bijan Najafi, Kamiar Aminian, Anisoara Paraschiv-Ionescu et al. · IEEE Transactions on Biomedical Engineering · 2003 · 766 citations · Full text