Heart Rate and Accelerometer Data Fusion for Activity Assessment of Rescuers During Emergency Interventions

The current state of the art in wearable electronics is the integration of very small devices into textile fabrics, the so-called ¿smart garment.¿ The ProeTEX project is one of many initiatives dedicated to the development of smart garments specifically designed for people who risk their lives in the line of duty such as fire fighters and Civil Protection rescuers. These garments have integrated multipurpose sensors that monitor their activities while in action. To this aim, we have developed an algorithm that combines both features extracted from the signal of a triaxial accelerometer and one ECG lead. Microprocessors integrated in the garments detect the signal magnitude area of inertial acceleration, step frequency, trunk inclination, heart rate (HR), and HR trend in real time. Given these inputs, a classifier assigns these signals to nine classes differentiating between certain physical activities (walking, running, moving on site), intensities (intense, mild, or at rest) and postures (lying down, standing up). Specific classes will be identified as dangerous to the rescuer during operation, such as, ¿subject motionless lying down¿ or ¿subject resting with abnormal HR.¿ Laboratory tests were carried out on seven healthy adult subjects with the collection of over 4.5 h of data. The results were very positive, achieving an overall classification accuracy of 88.8%.

[1]  Johannes Peltola,et al.  Activity classification using realistic data from wearable sensors , 2006, IEEE Transactions on Information Technology in Biomedicine.

[2]  Wan-Young Chung,et al.  A Fusion Health Monitoring Using ECG and Accelerometer sensors for Elderly Persons at Home , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Giovanni Magenes,et al.  Long-distance monitoring of physiological and environmental parameters for emergency operators , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  A K Bourke,et al.  Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. , 2007, Gait & posture.

[5]  D L Smith,et al.  Comparison of accelerometers with oxygen consumption in older adults during exercise. , 1999, Medicine and science in sports and exercise.

[6]  D. Swain,et al.  Relationship between % heart rate reserve and % VO2 reserve in treadmill exercise. , 1998, Medicine and science in sports and exercise.

[7]  Maarit Kangas,et al.  Comparison of low-complexity fall detection algorithms for body attached accelerometers. , 2008, Gait & posture.

[8]  J. Luprano,et al.  Smart Garments for Safety Improvement of Emergency/Disaster Operators , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  K. Aminian,et al.  Physical activity monitoring based on accelerometry: validation and comparison with video observation , 1999, Medical & Biological Engineering & Computing.

[10]  M. Lauer,et al.  Heart-rate recovery immediately after exercise as a predictor of mortality. , 1999, The New England journal of medicine.

[11]  K. Aminian,et al.  Temporal feature estimation during walking using miniature accelerometers: an analysis of gait improvement after hip arthroplasty , 1999, Medical & Biological Engineering & Computing.

[12]  Agustinus Borgy Waluyo,et al.  Ambulatory monitoring of human posture and walking speed using wearable accelerometer sensors , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Jon Rigelsford,et al.  Modelling and Control of Robot Manipulators , 2000 .

[14]  Heinz Theres,et al.  Utility of an Accelerometer Sensor Integrated into a Holter Monitoring System , 2000 .

[15]  Mark Latt,et al.  Walking speed, cadence and step length are selected to optimize the stability of head and pelvis accelerations , 2007, Experimental Brain Research.

[16]  M. Karvonen,et al.  The effects of training on heart rate; a longitudinal study. , 1957, Annales medicinae experimentalis et biologiae Fenniae.

[17]  U. Ekelund,et al.  Reliability and validity of the combined heart rate and movement sensor Actiheart , 2005, European Journal of Clinical Nutrition.

[18]  D. Rossi,et al.  Wearable kinesthetic system for capturing and classifying upper limb gesture in post-stroke rehabilitation , 2005, First Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. World Haptics Conference.

[19]  P.H. Veltink,et al.  Inclination measurement of human movement using a 3-D accelerometer with autocalibration , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  J. D. Janssen,et al.  A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity , 1997, IEEE Transactions on Biomedical Engineering.

[21]  L. Siciliano.  Modelling and Control of Robot Manipulators , 2000 .

[22]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[23]  D. De Rossi,et al.  Development of a novel algorithm for human fall detection using wearable sensors , 2008, 2008 IEEE Sensors.

[24]  Karsten Froberg,et al.  Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity. , 2007, Journal of applied physiology.

[25]  D.V. Thiel,et al.  Measurement of Energy Expenditure in Elite Athletes Using MEMS-Based Triaxial Accelerometers , 2007, IEEE Sensors Journal.

[26]  Dong-Hwan Hwang,et al.  A Step, Stride and Heading Determination for the Pedestrian Navigation System , 2004 .

[27]  Sundaresan Jayaraman,et al.  The Wearable Motherboard™: The first generation of adaptive and responsive textile structures (ARTS) for medical applications , 1999, Virtual Reality.

[28]  R. Casaburi,et al.  Normal cardiopulmonary responses during incremental exercise in 20- to 70-yr-old men. , 1994, Medicine and science in sports and exercise.

[29]  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.

[30]  Kent Larson,et al.  Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor , 2007, 2007 11th IEEE International Symposium on Wearable Computers.

[31]  D. Swain,et al.  Use of heart rate reserve and rating of perceived exertion to prescribe exercise intensity in diabetic autonomic neuropathy. , 2003, Diabetes care.

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

[33]  V F Froelicher,et al.  Heart rate recovery: validation and methodologic issues. , 2001, Journal of the American College of Cardiology.

[34]  Robert A. Robergs,et al.  The surprising history of the "HRmax=220-age" equation , 2002 .

[35]  Jennifer Healey,et al.  Wearable wellness monitoring using ECG and accelerometer data , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).