Technologies for Motion Measurements in Connected Health Scenario

Connected Health, also known as Technology-Enabled Care (TEC), refers to a conceptual model for health management where devices, services, or interventions are designed around the patient’s needs and health-related data is shared in such a way that the patient can receive care in the most proactive and efficient manner. In particular, TEC enables the remote exchange of information, mainly between a patient and a healthcare professional, to monitor health status, and to assist in diagnosis. To that aim recent advances in pervasive sensing, mobile, and communication technologies have led to the deployment of new smart sensors that can be worn without affecting a person’s daily activities. This chapter encompasses a brief literature review on TEC challenges, with a focus on the key technologies enabling the development of wearable solutions for remote human motion tracking. A wireless sensor network-based remote monitoring system, together with the main challenges and limitations that are likely to be faced during its implementation is also discussed, with a glimpse at its application.

[1]  Enrique Dorronzoro Zubiete,et al.  Review of wireless sensors networks in health applications , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Robert B. McGhee,et al.  A Simplified Quaternion-Based Algorithm for Orientation Estimation From Earth Gravity and Magnetic Field Measurements , 2008, IEEE Transactions on Instrumentation and Measurement.

[3]  Carlos A. Pomalaza-Raez,et al.  Design and Evaluation of a Wireless Body Sensor System for Smart Home Health Monitoring , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[4]  Daniel James,et al.  Accelerometers: An underutilized resource in sports monitoring , 2010, 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[5]  Pasquale Daponte,et al.  Improving the Orientation Estimation in a Packet Loss-affected Wireless Sensor Network for Tracking Human Motion , 2016 .

[6]  Michele Zorzi,et al.  Health care applications: a solution based on the internet of things , 2011, ISABEL '11.

[7]  Neil M. White,et al.  Augmenting forearm crutches with wireless sensors for lower limb rehabilitation , 2010 .

[8]  Robert B. McGhee,et al.  An extended Kalman filter for quaternion-based orientation estimation using MARG sensors , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[9]  Kay Soon Low,et al.  Unrestrained Measurement of Arm Motion Based on a Wearable Wireless Sensor Network , 2010, IEEE Transactions on Instrumentation and Measurement.

[10]  Yoneo Yano,et al.  Remote Training-Support of Running Form for Runners with Wireless Sensor , 2010 .

[11]  Pasquale Daponte,et al.  Design and validation of a motion-tracking system for ROM measurements in home rehabilitation , 2014 .

[12]  M A Brodie,et al.  The static accuracy and calibration of inertial measurement units for 3D orientation , 2008, Computer methods in biomechanics and biomedical engineering.

[13]  Shyamal Patel,et al.  A review of wearable sensors and systems with application in rehabilitation , 2012, Journal of NeuroEngineering and Rehabilitation.

[14]  Luca De Vito,et al.  Measurements and sensors for motion tracking in motor rehabilitation , 2014, IEEE Instrumentation & Measurement Magazine.

[15]  Abbas Jamalipour,et al.  Wireless Body Area Networks: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[16]  Pasquale Daponte,et al.  A wireless-based home rehabilitation system for monitoring 3D movements , 2013, 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[17]  Luca De Vito,et al.  One-Way Delay Measurement: State of Art , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[18]  Patrick Boissy,et al.  Capturing whole-body mobility of patients with Parkinson disease using inertial motion sensors: Expected challenges and rewards , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  P. Daponte,et al.  Electronic measurements in rehabilitation , 2011, 2011 IEEE International Symposium on Medical Measurements and Applications.

[20]  Pasquale Daponte,et al.  Validation of a home rehabilitation system for range of motion measurements of limb functions , 2013, 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[21]  Li Wei,et al.  A Practical Tool for Visualizing and Data Mining Medical Time Series , 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[22]  Salvatore Sessa,et al.  Waseda Bioinstrumentation System #3 as a tool for objective rehabilitation measurement and assessment - Development of the inertial measurement unit - , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.

[23]  Marco Bazzani,et al.  Enabling the IoT Paradigm in E-health Solutions through the VIRTUS Middleware , 2012, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications.

[24]  Holger Regenbrecht,et al.  Towards Pervasive Augmented Reality: Context-Awareness in Augmented Reality , 2017, IEEE Transactions on Visualization and Computer Graphics.

[25]  Alan F. Smeaton,et al.  Aggregating multiple body sensors for analysis in sports , 2008 .

[26]  Pasquale Daponte,et al.  Investigating the on-board data processing for IMU-based sensors in motion tracking for rehabilitation , 2015, 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings.

[27]  Luca De Vito,et al.  An IoT-enabled multi-sensor multi-user system for human motion measurements , 2017, 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[28]  Mark V. Williams,et al.  Rehospitalizations among patients in the Medicare fee-for-service program. , 2009, The New England journal of medicine.

[29]  Ashley N. Johnson,et al.  Dual-task motor performance with a tongue-operated assistive technology compared with hand operations , 2012, Journal of NeuroEngineering and Rehabilitation.

[30]  Gonzalo Mateos,et al.  Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges , 2015, 2015 IEEE International Conference on Services Computing.