A real-time algorithm based on triaxial accelerometer for the detection of human activity state

Authors:
Na Li;Yibin Hou;Zhangqin Huang
Affiliations:
Beijing University of technology, Beijing, China;Beijing University of technology, Beijing, China;Beijing University of technology, Beijing, China
Venue:
Proceedings of the 6th International Conference on Body Area Networks
Year:
2011

Citing 7
Cited 1

Activity recognition from accelerometer data

IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Flexible Cache Consistency Maintenance over Wireless Ad Hoc Networks

IEEE Transactions on Parallel and Distributed Systems
A real-time and self-calibrating algorithm based on triaxial accelerometer signals for the detection of human posture and activity

IEEE Transactions on Information Technology in Biomedicine
A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer

IEEE Transactions on Information Technology in Biomedicine
Human activity analysis: A review

ACM Computing Surveys (CSUR)
Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring

IEEE Transactions on Information Technology in Biomedicine
Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions

IEEE Transactions on Information Technology in Biomedicine

An event-driven energy efficient framework for wearable health-monitoring system

AMT'12 Proceedings of the 8th international conference on Active Media Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Assessment and identification of human activity and posture with triaxial accelerometer can provide information about the health state and the chronic care. This paper proposes a human state recognition algorithm based on Kalman filter (SRKF), which could identify steady state and state transition in real time. In this study, an automatic state-recognition system consisting of a Bluetooth module and a smart phone is developed. The Bluetooth module with a triaxial accelerometer was placed on the body and the raw sensor data was transported to and processed on the smart phone. Associated kinematics characteristic of human activity and the accelerometer signal, the results of amplitude function of vector CSVM were processed by Kalman filter to identify human state. Data were collected from ten adults in unsupervised environment. Experiment result shows that the algorithm has achieved better performance on the smart phone with limited computing and storage capacity.