TY - GEN
T1 - Quantitative assessment of lower limb and cane movement with wearable inertial sensors
AU - Sprint, Gina
AU - Cook, Diane J.
AU - Weeks, Douglas L.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/4/18
Y1 - 2016/4/18
N2 - Individuals with an age, injury, or disease-related mobility impairment often utilize a walking aid, such as a cane, to increase safety and stability during ambulation. Many individuals use a cane incorrectly and demonstrate altered gait patterns. Consequently, measuring the relationship between cane use and gait characteristics has potential to provide users, clinicians, and caregivers insightful information about cane-assisted walking. In this paper, we investigate fine-grained, objective measures of cane movement acquired from wearable inertial sensors. Specifically, we compute quantifications of swing and stance variability for both lower limbs and a cane device. We also introduce a novel visualization, the stance and swing phase plot, to facilitate insights into the sensor data. The computed gait parameters and visualization can potentially inform users and clinicians about assistive device usage over time and provide feedback about correct movement. We demonstrate the utility of the proposed algorithms with inertial sensor data collected from two patients undergoing inpatient stroke rehabilitation.
AB - Individuals with an age, injury, or disease-related mobility impairment often utilize a walking aid, such as a cane, to increase safety and stability during ambulation. Many individuals use a cane incorrectly and demonstrate altered gait patterns. Consequently, measuring the relationship between cane use and gait characteristics has potential to provide users, clinicians, and caregivers insightful information about cane-assisted walking. In this paper, we investigate fine-grained, objective measures of cane movement acquired from wearable inertial sensors. Specifically, we compute quantifications of swing and stance variability for both lower limbs and a cane device. We also introduce a novel visualization, the stance and swing phase plot, to facilitate insights into the sensor data. The computed gait parameters and visualization can potentially inform users and clinicians about assistive device usage over time and provide feedback about correct movement. We demonstrate the utility of the proposed algorithms with inertial sensor data collected from two patients undergoing inpatient stroke rehabilitation.
UR - http://www.scopus.com/inward/record.url?scp=84968546859&partnerID=8YFLogxK
U2 - 10.1109/BHI.2016.7455923
DO - 10.1109/BHI.2016.7455923
M3 - Conference contribution
AN - SCOPUS:84968546859
T3 - 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
SP - 418
EP - 421
BT - 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Y2 - 24 February 2016 through 27 February 2016
ER -