Monitoring Insole (MONI): A Low Power Solution Toward Daily Gait Monitoring and Analysis

Gait monitoring and analysis has attracted accumulative attention from the health technology community, with a particular high demand on wearable devices enabling long-term real-time daily gait monitoring. However, the vast majority of existing commercial and emerging technologies use significant electric power, need multiple sensor nodes, and are expensive and complicated to wear. This paper proposes a customized low power, low-cost monitoring insole (MONI) with minimized sensor nodes for real time daily gait monitoring and analysis in an accurate and reliable manner. With one accelerometer positioned at the heel and one at the first metatarsal, key gait features can be extracted and recorded in real-time. Selected gait information will be constructed as personalized daily gait traces to provide a low-power wearable solution for observing user’s gait evolution, which is critical for disease detection at early stages, such as Parkinson’s disease. To minimize the power consumption while efficiently collecting scattered gait information throughout the day, a working-mode management (WMM) algorithm, and an auto activity recognition (AAR) algorithm are developed to intelligently sample the right amount of true walking gait data in real-time. MONI is tested and validated both in the lab (with an accuracy of over 88.74% and a strong correlation intra class correlations (ICC) over 0.83). A pilot study is conducted in the field, suggesting less than 2-mW daily power consumption for 5500 steps of walking for an office worker.

[1]  Majid Sarrafzadeh,et al.  HERMES: Mobile system for instability analysis and balance assessment , 2013, TECS.

[2]  Joseph A. Paradiso,et al.  Gait Analysis Using a Shoe-Integrated Wireless Sensor System , 2008, IEEE Transactions on Information Technology in Biomedicine.

[3]  Jean-Louis Croisier,et al.  Contribution of a Trunk Accelerometer System to the Characterization of Gait in Patients With Mild-to-Moderate Parkinson's Disease , 2015, IEEE Journal of Biomedical and Health Informatics.

[4]  Wenyao Xu,et al.  Smart Insole: A Wearable Sensor Device for Unobtrusive Gait Monitoring in Daily Life , 2016, IEEE Transactions on Industrial Informatics.

[5]  F. Horak,et al.  iTUG, a Sensitive and Reliable Measure of Mobility , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Gu-Min Jeong,et al.  Stride Counting in Human Walking and Walking Distance Estimation Using Insole Sensors , 2016, Sensors.

[7]  K. McGraw,et al.  Forming inferences about some intraclass correlation coefficients. , 1996 .

[8]  Alessio Vecchio,et al.  Personalized gait detection using a wrist-worn accelerometer , 2017, 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[9]  Björn Eskofier,et al.  Inertial Sensor-Based Stride Parameter Calculation From Gait Sequences in Geriatric Patients , 2015, IEEE Transactions on Biomedical Engineering.

[10]  M. Gioulis,et al.  Gait analysis and clinical correlations in early Parkinson's disease. , 2017, Functional neurology.

[11]  Mark S. Redfern,et al.  Extraction of Stride Events From Gait Accelerometry During Treadmill Walking , 2015, IEEE Journal of Translational Engineering in Health and Medicine.

[12]  Toshiki Kobayashi,et al.  Kinetic Gait Analysis Using a Low-Cost Insole , 2013, IEEE Transactions on Biomedical Engineering.

[13]  Ravi Sankar,et al.  Gait monitoring system for patients with Parkinson's disease using wearable sensors , 2016, 2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT).

[14]  Allou Samé,et al.  Recognition of gait cycle phases using wearable sensors , 2016, Robotics Auton. Syst..

[15]  Jeffrey M. Hausdorff,et al.  Dual tasking, gait rhythmicity, and Parkinson's disease: Which aspects of gait are attention demanding? , 2005, The European journal of neuroscience.

[16]  Nicholas Wickström,et al.  A new measure of movement symmetry in early Parkinson's disease patients using symbolic processing of inertial sensor data , 2011, IEEE Transactions on Biomedical Engineering.

[17]  Isaac Skog,et al.  Zero-Velocity Detection—An Algorithm Evaluation , 2010, IEEE Transactions on Biomedical Engineering.

[18]  Jiaxin Ma,et al.  Optimizing Gait Parameters and Insole Sensor Positioning for Parkinson's Disease Assessment , 2017 .

[19]  Jonghyun Kim,et al.  An Ambulatory Gait Monitoring System with Activity Classification and Gait Parameter Calculation Based on a Single Foot Inertial Sensor , 2018, IEEE Transactions on Biomedical Engineering.

[20]  Yosuke Kurihara,et al.  Accelerometry-Based Gait Analysis and Its Application to Parkinson's Disease Assessment— Part 2 : A New Measure for Quantifying Walking Behavior , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[21]  Saeid Sanei,et al.  A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications , 2018, IEEE Reviews in Biomedical Engineering.

[22]  A Leardini,et al.  Estimation of spatial-temporal gait parameters in level walking based on a single accelerometer: Validation on normal subjects by standard gait analysis , 2012, Comput. Methods Programs Biomed..

[23]  Jeffrey M. Hausdorff,et al.  Rhythmic auditory stimulation modulates gait variability in Parkinson's disease , 2007, The European journal of neuroscience.

[24]  Saman K. Halgamuge,et al.  Classification of Parkinson's Disease Gait Using Spatial-Temporal Gait Features , 2015, IEEE Journal of Biomedical and Health Informatics.

[25]  Ruzena Bajcsy,et al.  Results of Using a Wireless Inertial Measuring System to Quantify Gait Motions in Control Subjects , 2010, IEEE Transactions on Information Technology in Biomedicine.

[26]  Valentina Agostini,et al.  Segmentation and Classification of Gait Cycles , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  Dag Nyholm,et al.  Using measurements from wearable sensors for automatic scoring of Parkinson's disease motor states: Results from 7 patients , 2017, EMBC.

[28]  Kun-Chan Lan,et al.  Gait Monitoring for Early Neurological Disorder Detection Using Sensors in a Smartphone: Validation and a Case Study of Parkinsonism. , 2016, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[29]  Edward Sazonov,et al.  Monitoring of Posture Allocations and Activities by a Shoe-Based Wearable Sensor , 2011, IEEE Transactions on Biomedical Engineering.

[30]  J. Winkler,et al.  Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease , 2013, PloS one.

[31]  Jeffrey M. Hausdorff,et al.  Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson's disease , 2005, Movement disorders : official journal of the Movement Disorder Society.

[32]  Hongnian Yu,et al.  Optimal Foot Location for Placing Wearable IMU Sensors and Automatic Feature Extraction for Gait Analysis , 2018, IEEE Sensors Journal.

[33]  M. Batavia,et al.  The validity and reliability of the GAITRite system's measurements: A preliminary evaluation. , 2001, Archives of physical medicine and rehabilitation.

[34]  Levi J. Hargrove,et al.  Gait Characteristics When Walking on Different Slippery Walkways , 2016, IEEE Transactions on Biomedical Engineering.

[35]  Alberto J. Palma,et al.  Embedded sensor insole for wireless measurement of gait parameters , 2013, Australasian Physical & Engineering Sciences in Medicine.

[36]  Catherine Dehollain,et al.  Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring , 2004, IEEE Transactions on Biomedical Engineering.

[37]  David M. Morris,et al.  The Pediatric SmartShoe: Wearable Sensor System for Ambulatory Monitoring of Physical Activity and Gait , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[38]  Björn Eskofier,et al.  Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson's disease , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.