A Comparative Review of Footwear-Based Wearable Systems

Footwear is an integral part of daily life. Embedding sensors and electronics in footwear for various different applications started more than two decades ago. This review article summarizes the developments in the field of footwear-based wearable sensors and systems. The electronics, sensing technologies, data transmission, and data processing methodologies of such wearable systems are all principally dependent on the target application. Hence, the article describes key application scenarios utilizing footwear-based systems with critical discussion on their merits. The reviewed application scenarios include gait monitoring, plantar pressure measurement, posture and activity classification, body weight and energy expenditure estimation, biofeedback, navigation, and fall risk applications. In addition, energy harvesting from the footwear is also considered for review. The article also attempts to shed light on some of the most recent developments in the field along with the future work required to advance the field.

[1]  Nigel H. Lovell,et al.  Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring , 2006, IEEE Transactions on Information Technology in Biomedicine.

[2]  M. J. Muêller,et al.  Application of plantar pressure assessment in footwear and insert design. , 1999, The Journal of orthopaedic and sports physical therapy.

[3]  S Ino,et al.  An in-shoe device to measure plantar pressure during daily human activity. , 2011, Medical engineering & physics.

[4]  R. Ferber,et al.  Validation of Plantar Pressure Measurements for a Novel in-Shoe Plantar Sensory Replacement Unit , 2013, Journal of diabetes science and technology.

[5]  Gregory J Welk,et al.  Validity of consumer-based physical activity monitors. , 2014, Medicine and science in sports and exercise.

[6]  Raymond C Browning,et al.  A comparison of energy expenditure estimation of several physical activity monitors. , 2013, Medicine and science in sports and exercise.

[7]  Edward Sazonov,et al.  Accurate prediction of energy expenditure using a shoe-based activity monitor. , 2011, Medicine and science in sports and exercise.

[8]  Panadda Marayong,et al.  Design of a biofeedback device for gait rehabilitation in post-stroke patients , 2015, 2015 IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS).

[9]  Álvaro Herrero,et al.  Features and models for human activity recognition , 2015, Neurocomputing.

[10]  Said Ahmaidi,et al.  Gait cycle and plantar pressure distribution in children with cerebral palsy: clinically useful outcome measures for a management and rehabilitation. , 2014, NeuroRehabilitation.

[11]  Lorenzo Chiari,et al.  A Mobile Kalman-Filter Based Solution for the Real-Time Estimation of Spatio-Temporal Gait Parameters , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Ming-Chun Huang,et al.  An Energy-Efficient Adaptive Sensing Framework for Gait Monitoring Using Smart Insole , 2015, IEEE Sensors Journal.

[13]  Sheikh Iqbal Ahamed,et al.  smartPrediction: a real-time smartphone-based fall risk prediction and prevention system , 2013, RACS.

[14]  J. Hart,et al.  Effects of an auditory biofeedback device on plantar pressure in patients with chronic ankle instability. , 2016, Gait & posture.

[15]  M. Tomizuka,et al.  A Gait Monitoring System Based on Air Pressure Sensors Embedded in a Shoe , 2009, IEEE/ASME Transactions on Mechatronics.

[16]  Edward Sazonov,et al.  Automatic Detection of Temporal Gait Parameters in Poststroke Individuals , 2011, IEEE Transactions on Information Technology in Biomedicine.

[17]  Tim Olds,et al.  The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study , 2015, International Journal of Behavioral Nutrition and Physical Activity.

[18]  Björn Eskofier,et al.  Stride Segmentation during Free Walk Movements Using Multi-Dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data , 2015, Sensors.

[19]  Edward Sazonov,et al.  Development of the RT-GAIT, a Real-Time feedback device to improve Gait of individuals with stroke , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  B. G. Celler,et al.  Classification of basic daily movements using a triaxial accelerometer , 2004, Medical and Biological Engineering and Computing.

[21]  Edward S. Sazonov,et al.  Prediction of Bodyweight and Energy Expenditure Using Point Pressure and Foot Acceleration Measurements , 2011, The open biomedical engineering journal.

[22]  Ting Zhang,et al.  Measuring gait symmetry in children with cerebral palsy using the SmartShoe , 2014, 2014 IEEE Healthcare Innovation Conference (HIC).

[23]  Edward Sazonov,et al.  Using Sensors to Measure Activity in People with Stroke , 2011, Topics in stroke rehabilitation.

[24]  M. Mathie,et al.  Detection of daily physical activities using a triaxial accelerometer , 2003, Medical and Biological Engineering and Computing.

[25]  C. Maganaris,et al.  The effects of soccer training and timing of balance training on balance ability , 2006, European Journal of Applied Physiology.

[26]  Franz Konstantin Fuss,et al.  Design of Low Cost Smart Insole for Real Time Measurement of Plantar Pressure , 2015 .

[27]  Matthias Struck,et al.  User-friendly system for recognition of activities with an accelerometer , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[28]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[29]  Iván González,et al.  An Ambulatory System for Gait Monitoring Based on Wireless Sensorized Insoles , 2015, Sensors.

[30]  Xuegang Wang,et al.  A foot-wearable interface for locomotion mode recognition based on discrete contact force distribution , 2015 .

[31]  François Rioux,et al.  Validation and Reliability of a Classification Method to Measure the Time Spent Performing Different Activities , 2015, PloS one.

[32]  Qiao Li,et al.  In-Shoe Plantar Pressure Measurement and Analysis System Based on Fabric Pressure Sensing Array , 2010, IEEE Transactions on Information Technology in Biomedicine.

[33]  Joseph A. Paradiso,et al.  Parasitic power harvesting in shoes , 1998, Digest of Papers. Second International Symposium on Wearable Computers (Cat. No.98EX215).

[34]  Scott E. Crouter,et al.  Validity of Consumer-Based Physical Activity Monitors for Estimating Energy Expenditure in Youth , 2017 .

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

[36]  James O. Hill,et al.  Obesity and the Environment: Where Do We Go from Here? , 2003, Science.

[37]  Luca Benini,et al.  A wireless system for gait and posture analysis based on pressure insoles and Inertial Measurement Units , 2009, 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare.

[38]  L. Berglund,et al.  Validation of F-Scan pressure sensor system: a technical note. , 1998, Journal of rehabilitation research and development.

[39]  S. Simon.  Gait Analysis, Normal and Pathological Function. , 1993 .

[40]  E. Villanueva.  The validity of self-reported weight in US adults: a population based cross-sectional study , 2001, BMC public health.

[41]  Claudia Giacomozzi,et al.  Appropriateness of plantar pressure measurement devices: a comparative technical assessment. , 2010, Gait & posture.

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

[43]  Sheikh Iqbal Ahamed,et al.  A Novel Activity Detection System Using Plantar Pressure Sensors and Smartphone , 2015, 2015 IEEE 39th Annual Computer Software and Applications Conference.

[44]  Joseph A. Paradiso,et al.  Energy Scavenging with Shoe-Mounted Piezoelectrics , 2001, IEEE Micro.

[45]  M. Clark,et al.  Interindividual Variation in Posture Allocation: Possible Role in Human Obesity , 2005, Science.

[46]  Edward Sazonov,et al.  Highly Accurate Recognition of Human Postures and Activities Through Classification With Rejection , 2014, IEEE Journal of Biomedical and Health Informatics.

[47]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[48]  Kirk E. Smith,et al.  Forefoot structural predictors of plantar pressures during walking in people with diabetes and peripheral neuropathy. , 2003, Journal of biomechanics.

[49]  Martin J.-D. Otis,et al.  Toward an augmented shoe for preventing falls related to physical conditions of the soil , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[50]  T. McPoil,et al.  Plantar pressure assessment. , 2000, Physical therapy.

[51]  Kamiar Aminian,et al.  On-Shoe Wearable Sensors for Gait and Turning Assessment of Patients With Parkinson's Disease , 2013, IEEE Transactions on Biomedical Engineering.

[52]  Edward Sazonov,et al.  Development of SmartStep: An insole-based physical activity monitor , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[53]  Yangsheng Xu,et al.  Intelligent shoes for abnormal gait detection , 2008, 2008 IEEE International Conference on Robotics and Automation.

[54]  Matjaz Gams,et al.  Context-based ensemble method for human energy expenditure estimation , 2015, Appl. Soft Comput..

[55]  Xu Xu,et al.  Accuracy of the Microsoft Kinect for measuring gait parameters during treadmill walking. , 2015, Gait & posture.

[56]  Manos M. Tentzeris,et al.  “Smart Shoe”: An autonomous inkjet-printed RFID system scavenging walking energy , 2011, 2011 IEEE International Symposium on Antennas and Propagation (APSURSI).

[57]  Yangsheng Xu,et al.  Gait Modeling for Human Identification , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[58]  N. A. Abu Osman,et al.  Comparing Manufacturer's Point Calibration and Modified Calibration Setup for F-Scan Insole Sensor System: A Preliminary Assessment , 2008 .

[59]  Syed Mahfuzul Aziz,et al.  Identification of Foot Pathologies Based on Plantar Pressure Asymmetry , 2015, Sensors.

[60]  Tao Liu,et al.  A wearable force plate system for the continuous measurement of triaxial ground reaction force in biomechanical applications , 2010 .

[61]  Ilias Maglogiannis,et al.  Advanced patient or elder fall detection based on movement and sound data , 2008, 2008 Second International Conference on Pervasive Computing Technologies for Healthcare.

[62]  Ran Gilad-Bachrach,et al.  Full body gait analysis with Kinect , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[63]  Martin Ekström,et al.  Wearable Weight Estimation System , 2015 .

[64]  Jingjing Zhao,et al.  A Shoe-Embedded Piezoelectric Energy Harvester for Wearable Sensors , 2014, Sensors.

[65]  A. Haslett.  Electronics , 1948 .

[66]  Tao Liu,et al.  A Wearable Ground Reaction Force Sensor System and Its Application to the Measurement of Extrinsic Gait Variability , 2010, Sensors.

[67]  Marimuthu Palaniswami,et al.  Computational intelligence for movement sciences : neural networks and other emerging techniques , 2006 .

[68]  Martin J.-D. Otis,et al.  An efficient home-based risk of falling assessment test based on Smartphone and instrumented insole , 2015, 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings.

[69]  Grant Trewartha,et al.  Time-based calibrations of pressure sensors improve the estimation of force signals containing impulsive events , 2014 .

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

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

[72]  Edward Sazonov,et al.  Development of a real time activity monitoring Android application utilizing SmartStep , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[73]  Sylvie Lamy-Perbal,et al.  An improved shoe-mounted inertial navigation system , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[74]  K. S. Park,et al.  Fall detection algorithm for the elderly using acceleration sensors on the shoes , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[75]  Edward Sazonov,et al.  SmartStep: A Fully Integrated, Low-Power Insole Monitor , 2014 .

[76]  Evangelos Pappas,et al.  A comparison of energy expenditure estimates from the Actiheart and Actical physical activity monitors during low intensity activities, walking, and jogging , 2010, European Journal of Applied Physiology.

[77]  Gamze Uslu,et al.  RAM: Real Time Activity Monitoring with feature extractive training , 2015, Expert Syst. Appl..

[78]  S. Marocco,et al.  Classification of plantar pressure and heel acceleration patterns using neural networks , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[79]  Ting Zhang,et al.  Using decision trees to measure activities in people with stroke , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[80]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[81]  Edward Sazonov,et al.  Posture and Activity Recognition and Energy Expenditure Estimation in a Wearable Platform , 2015, IEEE Journal of Biomedical and Health Informatics.

[82]  W. Garrett,et al.  Forefoot Loading during 3 Athletic Tasks , 2007, The American journal of sports medicine.

[83]  B. Najafi,et al.  Sensor-Derived Physical Activity Parameters Can Predict Future Falls in People with Dementia , 2014, Gerontology.

[84]  Simona Crea,et al.  A Wireless Flexible Sensorized Insole for Gait Analysis , 2014, Sensors.

[85]  Edward Sazonov,et al.  SmartStep 2.0 - A completely wireless, versatile insole monitoring system , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[86]  Joseph A. Paradiso,et al.  Shoe-integrated sensor system for wireless gait analysis and real-time feedback , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[87]  Ozkan Bebek,et al.  Personal Navigation via High-Resolution Gait-Corrected Inertial Measurement Units , 2010, IEEE Transactions on Instrumentation and Measurement.

[88]  Edward Sazonov,et al.  Recognition of household and athletic activities using smartshoe , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[89]  N. Jarboe,et al.  Assessment of pedar and F-Scan revisited. , 1997, Clinical biomechanics.

[90]  Maximilian Schirmer,et al.  Shoe me the Way: A Shoe-Based Tactile Interface for Eyes-Free Urban Navigation , 2015, MobileHCI.

[91]  Edward Sazonov,et al.  Identifying Activity Levels and Steps of People With Stroke Using a Novel Shoe-Based Sensor , 2012, Journal of neurologic physical therapy : JNPT.

[92]  Patrick Chiang,et al.  A piezoelectric energy-harvesting shoe system for podiatric sensing , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[93]  Alfred D. Grant.  Gait Analysis: Normal and Pathological Function , 2010 .

[94]  S. Bamberg,et al.  Utilization of a lower extremity ambulatory feedback system to reduce gait asymmetry in transtibial amputation gait. , 2012, Gait & posture.

[95]  R J Abboud,et al.  Normal pressure values and repeatability of the Emed ST4 system. , 2008, Gait & posture.

[96]  Repeatability and Reproducibility of the F-Scan System in Healthy Children , 2016 .