Realtime recognition of complex human daily activities using human motion and location data

IEEE Transactions on Biomedical Engineering

Volumn 59, Issue 9, 2012, Pages 2422-2430

  Zhu, Chun ^a   Sheng, Weihua ^b  

^a MICROSOFT   (United States)

^b Oklahoma State University   (United States)

Author keywords

Activity recognition; body sensor network; dynamic Bayesian network (DBN); wearable computing

Indexed keywords

ACTIVITY RECOGNITION; BAYESIAN FILTERS; BODY ACTIVITIES; DAILY ACTIVITY; DYNAMIC BAYESIAN NETWORKS; HAND GESTURE; HUMAN MOTIONS; HUMAN SUBJECTS; INDOOR ENVIRONMENT; LIVING SYSTEMS; LOCATION DATA; LOCATION INFORMATION; MEMORY USAGE; MOTION SENSORS; OPTICAL MOTION CAPTURE; ORIENTATION SENSORS; POWER MANAGEMENT UNIT; POWER-AWARE; REAL TIME RECOGNITION; THREE-LEVEL; WEARABLE COMPUTING; WIRELESS COMMUNICATIONS;

BODY SENSOR NETWORKS; ENERGY MANAGEMENT; VITERBI ALGORITHM; WEARABLE COMPUTERS; WIRELESS TELECOMMUNICATION SYSTEMS;

SENSOR NODES;

ALGORITHM; ARTICLE; DAILY LIFE ACTIVITY; ENVIRONMENT; GESTURE; HAND MOVEMENT; HIP; HUMAN; HUMAN EXPERIMENT; MEMORY; MOTION; MOTION ANALYSIS SYSTEM; PHYSICAL ACTIVITY; RECOGNITION; RECUMBENCY; SENSOR; SITTING; STANDING; THIGH; WAIST; WALKING; WIRELESS COMMUNICATION;

ACTIVITIES OF DAILY LIVING; ALGORITHMS; BAYES THEOREM; CLOTHING; HUMANS; MOVEMENT; PATTERN RECOGNITION, AUTOMATED; TELEMETRY;

EID: 84865529748     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2012.2190602     Document Type: Article

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