Abstract
This paper presents the use of an accelerometry-based wearable motion detector for real-time recognizing gait cycle parameters of Parkinson's disease (PD) patients. The wearable motion detector uses a tri-axial accelerometer to measure trunk accelerations during walking. By using the autocorrelation procedure, several gait cycle parameters including cadence, gait regularity, and symmetry can be derived in real-time from the measured trunk acceleration data. The gait cycle parameters derived from 5 elder PD patients and 5 young healthy subjects are also compared. The measures of the gait cycle parameters between the PD patients and the healthy subjects are distinct and therefore can be quantified and distinguished, which indicates that detection of abnormal gaits of PD patients in real-time is also possible. The wearable motion detector developed in this paper is a practical system that enables quantitative and objective mobility assessment. The possible applications of this system are also discussed.
| Original language | English |
|---|---|
| Title of host publication | Proceedings 2011 International Conference on System Science and Engineering, ICSSE 2011 |
| Pages | 498-502 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - Aug 24 2011 |
| Externally published | Yes |
| Event | 2011 International Conference on System Science and Engineering, ICSSE 2011 - Macao, China Duration: Jun 8 2011 → Jun 10 2011 |
Conference
| Conference | 2011 International Conference on System Science and Engineering, ICSSE 2011 |
|---|---|
| Country | China |
| City | Macao |
| Period | 6/8/11 → 6/10/11 |
Fingerprint
Keywords
- accelerometer
- accelerometry
- gait
- mobility
- Parkinson's disease
ASJC Scopus subject areas
- Control and Systems Engineering
Cite this
Real-time gait cycle parameters recognition using a wearable motion detector. / Yang, Che Chang; Hsu, Yeh Liang; Lu, Jun Ming; Shih, Kao Shang; Chan, Lung.
Proceedings 2011 International Conference on System Science and Engineering, ICSSE 2011. 2011. p. 498-502 5961954.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Real-time gait cycle parameters recognition using a wearable motion detector
AU - Yang, Che Chang
AU - Hsu, Yeh Liang
AU - Lu, Jun Ming
AU - Shih, Kao Shang
AU - Chan, Lung
PY - 2011/8/24
Y1 - 2011/8/24
N2 - This paper presents the use of an accelerometry-based wearable motion detector for real-time recognizing gait cycle parameters of Parkinson's disease (PD) patients. The wearable motion detector uses a tri-axial accelerometer to measure trunk accelerations during walking. By using the autocorrelation procedure, several gait cycle parameters including cadence, gait regularity, and symmetry can be derived in real-time from the measured trunk acceleration data. The gait cycle parameters derived from 5 elder PD patients and 5 young healthy subjects are also compared. The measures of the gait cycle parameters between the PD patients and the healthy subjects are distinct and therefore can be quantified and distinguished, which indicates that detection of abnormal gaits of PD patients in real-time is also possible. The wearable motion detector developed in this paper is a practical system that enables quantitative and objective mobility assessment. The possible applications of this system are also discussed.
AB - This paper presents the use of an accelerometry-based wearable motion detector for real-time recognizing gait cycle parameters of Parkinson's disease (PD) patients. The wearable motion detector uses a tri-axial accelerometer to measure trunk accelerations during walking. By using the autocorrelation procedure, several gait cycle parameters including cadence, gait regularity, and symmetry can be derived in real-time from the measured trunk acceleration data. The gait cycle parameters derived from 5 elder PD patients and 5 young healthy subjects are also compared. The measures of the gait cycle parameters between the PD patients and the healthy subjects are distinct and therefore can be quantified and distinguished, which indicates that detection of abnormal gaits of PD patients in real-time is also possible. The wearable motion detector developed in this paper is a practical system that enables quantitative and objective mobility assessment. The possible applications of this system are also discussed.
KW - accelerometer
KW - accelerometry
KW - gait
KW - mobility
KW - Parkinson's disease
UR - http://www.scopus.com/inward/record.url?scp=84860421421&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84860421421&partnerID=8YFLogxK
U2 - 10.1109/ICSSE.2011.5961954
DO - 10.1109/ICSSE.2011.5961954
M3 - Conference contribution
AN - SCOPUS:84860421421
SN - 9781612844718
SP - 498
EP - 502
BT - Proceedings 2011 International Conference on System Science and Engineering, ICSSE 2011
ER -