TY - JOUR
T1 - CyclePro
T2 - A Robust Framework for Domain-Agnostic Gait Cycle Detection
AU - Ma, Yuchao
AU - Ashari, Zhila Esna
AU - Pedram, Mahdi
AU - Amini, Navid
AU - Tarquinio, Daniel
AU - Nouri-Mahdavi, Kouros
AU - Pourhomayoun, Mohammad
AU - Catena, Robert D.
AU - Ghasemzadeh, Hassan
N1 - Funding Information:
Manuscript received October 22, 2018; revised December 27, 2018; accepted January 2, 2019. Date of publication January 17, 2019; date of current version April 17, 2019. This work was supported in part by the National Science Foundation under Grant CNS-1566359 and Grant CNS-1750679, and in part by the Grant from Rett Syndrome Research Trust. The associate editor coordinating the review of this paper and approving it for publication was Prof. Chang-Hee Won. (Corresponding author: Zhila Esna Ashari.) Y. Ma, Z. Esna Ashari, M. Pedram, and H. Ghasemzadeh are with the School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164 USA (e-mail: yuchao.ma@ wsu.edu; z.esnaashariesfahan@wsu.edu; mahdi.pedram@wsu.edu; hassan. ghasemzadeh@wsu.edu).
Funding Information:
This work was supported in part by the National Science Foundation under Grant CNS-1566359 and Grant CNS-1750679, and in part by the Grant from Rett Syndrome Research Trust.
Publisher Copyright:
© 2001-2012 IEEE.
PY - 2019/5/15
Y1 - 2019/5/15
N2 - The utility of wearable sensors for continuous gait monitoring has grown substantially, enabling novel applications on mobility assessment in healthcare. Existing approaches for gait cycle detection rely on predefined or experimentally tuned platform parameters and are often platform specific, parameter sensitive, and unreliable in noisy environments with constrained generalizability. To address these challenges, we introduce CyclePro,1 novel framework for reliable and platform-independent gait cycle detection. CyclePro offers unique features: 1) it leverages physical properties of human gait to learn model parameters; 2) captured signals are transformed into signal magnitude and processed through a normalized cross-correlation module to compensate for noise and search for repetitive patterns without predefined parameters; and 3) an optimal peak detection algorithm is developed to accurately find strides within the motion sensor data. To demonstrate the efficiency of CyclePro, three experiments are conducted: a clinical study including a visually impaired group of patients with glaucoma and a control group of healthy participants; a clinical study involving children with Rett syndrome; and an experiment involving healthy participants. The performance of CyclePro is assessed under varying platform settings and demonstrates to maintain over 93% accuracy under noisy signal, varying bit resolutions, and changes in sampling frequency. This translates into a recall of 95.3% and a precision of 93.4%, on average. Moreover, CyclePro can detect strides and estimate cadence using data from different sensors, with accuracy higher than 95%, and it is robust to random sensor orientations with a recall of 91.5% and a precision of 99.2%, on average.1Software code and sample datasets for CyclePro are publicly available at https://github.com/y-max/CycleProa
AB - The utility of wearable sensors for continuous gait monitoring has grown substantially, enabling novel applications on mobility assessment in healthcare. Existing approaches for gait cycle detection rely on predefined or experimentally tuned platform parameters and are often platform specific, parameter sensitive, and unreliable in noisy environments with constrained generalizability. To address these challenges, we introduce CyclePro,1 novel framework for reliable and platform-independent gait cycle detection. CyclePro offers unique features: 1) it leverages physical properties of human gait to learn model parameters; 2) captured signals are transformed into signal magnitude and processed through a normalized cross-correlation module to compensate for noise and search for repetitive patterns without predefined parameters; and 3) an optimal peak detection algorithm is developed to accurately find strides within the motion sensor data. To demonstrate the efficiency of CyclePro, three experiments are conducted: a clinical study including a visually impaired group of patients with glaucoma and a control group of healthy participants; a clinical study involving children with Rett syndrome; and an experiment involving healthy participants. The performance of CyclePro is assessed under varying platform settings and demonstrates to maintain over 93% accuracy under noisy signal, varying bit resolutions, and changes in sampling frequency. This translates into a recall of 95.3% and a precision of 93.4%, on average. Moreover, CyclePro can detect strides and estimate cadence using data from different sensors, with accuracy higher than 95%, and it is robust to random sensor orientations with a recall of 91.5% and a precision of 99.2%, on average.1Software code and sample datasets for CyclePro are publicly available at https://github.com/y-max/CycleProa
KW - Rett syndrome
KW - Wearable computing
KW - gait cycle detection
KW - glaucoma
KW - reliability
UR - http://www.scopus.com/inward/record.url?scp=85064659467&partnerID=8YFLogxK
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U2 - 10.1109/JSEN.2019.2893225
DO - 10.1109/JSEN.2019.2893225
M3 - Article
AN - SCOPUS:85064659467
SN - 1530-437X
VL - 19
SP - 3751
EP - 3762
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 10
M1 - 8616844
ER -