A Smartphone Step Counter Using IMU and Magnetometer for Navigation and Health Monitoring Applications

The growing market of smart devices make them appealing for various applications. Motion tracking can be achieved using such devices, and is important for various applications such as navigation, search and rescue, health monitoring, and quality of life-style assessment. Step detection is a crucial task that affects the accuracy and quality of such applications. In this paper, a new step detection technique is proposed, which can be used for step counting and activity monitoring for health applications as well as part of a Pedestrian Dead Reckoning (PDR) system. Inertial and Magnetic sensors measurements are analyzed and fused for detecting steps under varying step modes and device pose combinations using a free-moving handheld device (smartphone). Unlike most of the state of the art research in the field, the proposed technique does not require a classifier, and adaptively tunes the filters and thresholds used without the need for presets while accomplishing the task in a real-time operation manner. Testing shows that the proposed technique successfully detects steps under varying motion speeds and device use cases with an average performance of 99.6%, and outperforms some of the state of the art techniques that rely on classifiers and commercial wristband products.

[1]  J. Borenstein,et al.  Non-GPS Navigation for Security Personnel and First Responders , 2007, Journal of Navigation.

[2]  Chris Hide,et al.  Understanding the performance of zero velocity updates in MEMS-based pedestrian navigation , 2014 .

[3]  Robert Piche,et al.  Uninterrupted portable car navigation system using GPS, map and inertial sensors data , 2009, 2009 IEEE 13th International Symposium on Consumer Electronics.

[4]  K. Volpp,et al.  Accuracy of smartphone applications and wearable devices for tracking physical activity data. , 2015, JAMA.

[5]  Robert Piché,et al.  A Survey of Selected Indoor Positioning Methods for Smartphones , 2017, IEEE Communications Surveys & Tutorials.

[6]  Roozbeh Jafari,et al.  Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications , 2013, IEEE Transactions on Human-Machine Systems.

[7]  Naser El-Sheimy,et al.  Smartphone Orientation Tracking Algorithm for Pedestrian Navigation , 2017 .

[8]  Kong Y Chen,et al.  Redefining the roles of sensors in objective physical activity monitoring. , 2012, Medicine and science in sports and exercise.

[9]  Phillip Tomé,et al.  Indoor Navigation of Emergency Agents , 2007 .

[10]  Claudia Linnhoff-Popien,et al.  Step and activity detection based on the orientation and scale attributes of the SURF algorithm , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[11]  Abdelmoumen Norrdine,et al.  Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet , 2016, IEEE Sensors Journal.

[12]  Agus Budiyono,et al.  Principles of GNSS, Inertial, and Multi-sensor Integrated Navigation Systems , 2012 .

[13]  Chris D. Nugent,et al.  Activity monitoring using an intelligent mobile phone: a validation study , 2010, PETRA '10.

[14]  Blaine A. Price,et al.  Wearables: has the age of smartwatches finally arrived? , 2015, Commun. ACM.

[15]  M.R. Popovic,et al.  A reliable gait phase detection system , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  J. Borenstein,et al.  Heuristic Reduction of Gyro Drift for Personnel Tracking Systems , 2009 .

[17]  Yun Pan,et al.  A Multi-Mode Dead Reckoning System for Pedestrian Tracking Using Smartphones , 2016, IEEE Sensors Journal.

[18]  Xiaoping Yun,et al.  Self-contained Position Tracking of Human Movement Using Small Inertial/Magnetic Sensor Modules , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[19]  A. Kriska,et al.  Gait speed and step-count monitor accuracy in community-dwelling older adults. , 2008, Medicine and science in sports and exercise.

[20]  J. Borenstein,et al.  Personal Dead-reckoning System for GPS-denied Environments , 2007, 2007 IEEE International Workshop on Safety, Security and Rescue Robotics.

[21]  Young Soo Suh,et al.  A Zero Velocity Detection Algorithm Using Inertial Sensors for Pedestrian Navigation Systems , 2010, Sensors.

[22]  Friedrich Keller,et al.  Development of a step counter based on artificial neural networks , 2016, J. Locat. Based Serv..

[23]  Teresa Zielińska,et al.  Gait features analysis using artificial neural networks - testing the footwear effect. , 2017, Acta of bioengineering and biomechanics.

[24]  Aboelmagd Noureldin,et al.  Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration , 2012 .

[25]  Mazharul Islam,et al.  Detection of Gait Modes Using an Artificial Neural Network during Walking with a Powered Ankle-Foot Orthosis , 2016, Journal of biophysics.

[26]  Per K. Enge,et al.  Global positioning system: signals, measurements, and performance [Book Review] , 2002, IEEE Aerospace and Electronic Systems Magazine.

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

[28]  Jean-Baptiste Pothin,et al.  An efficient fuzzy logic step detection algorithm for unconstrained smartphones , 2015, 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[29]  Senem Velipasalar,et al.  Robust and reliable step counting by mobile phone cameras , 2015, ICDSC.

[30]  Agathoniki Trigoni,et al.  Robust pedestrian dead reckoning (R-PDR) for arbitrary mobile device placement , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[31]  P. Robertson,et al.  Unscented Kalman filter and Magnetic Angular Rate Update (MARU) for an improved Pedestrian Dead-Reckoning , 2012, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.

[32]  Myeong-jin Lee,et al.  Step Detection Robust against the Dynamics of Smartphones , 2015, Sensors.

[33]  Fernando Seco Granja,et al.  Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[34]  Eun-Hwan Shin,et al.  A New Calibration Method for Strapdown Inertial Navigation Systems , 2022 .

[35]  Daniel Tazartes,et al.  An historical perspective on inertial navigation systems , 2014, 2014 International Symposium on Inertial Sensors and Systems (ISISS).

[36]  Jianming Wei,et al.  A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors , 2015, Sensors.

[37]  Margaret Grey,et al.  Motion Sensor Use for Physical Activity Data: Methodological Considerations , 2015, Nursing research.

[38]  KR Westerterp,et al.  Advances in physical activity monitoring and lifestyle interventions in obesity: a review , 2012, International Journal of Obesity.

[39]  Valérie Renaudin,et al.  Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users , 2013, Sensors.

[40]  C. Mazzà,et al.  Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors , 2015, PloS one.

[41]  Young Soo Suh,et al.  Pedestrian inertial navigation with gait phase detection assisted zero velocity updating , 2000, 2009 4th International Conference on Autonomous Robots and Agents.