Suitability of commercial barometric pressure sensors to distinguish sitting and standing activities for wearable monitoring.

Despite its medical relevance, accurate recognition of sedentary (sitting and lying) and dynamic activities (e.g. standing and walking) remains challenging using a single wearable device. Currently, trunk-worn wearable systems can differentiate sitting from standing with moderate success, as activity classifiers often rely on inertial signals at the transition period (e.g. from sitting to standing) which contains limited information. Discriminating sitting from standing thus requires additional sources of information such as elevation change. The aim of this study is to demonstrate the suitability of barometric pressure, providing an absolute estimate of elevation, for evaluating sitting and standing periods during daily activities. Three sensors were evaluated in both calm laboratory conditions and a pilot study involving seven healthy subjects performing 322 sitting and standing transitions, both indoor and outdoor, in real-world conditions. The MS5611-BA01 barometric pressure sensor (Measurement Specialties, USA) demonstrated superior performance to counterparts. It discriminates actual sitting and standing transitions from stationary postures with 99.5% accuracy and is also capable to completely dissociate Sit-to-Stand from Stand-to-Sit transitions.

[1]  Guang-Zhong Yang,et al.  Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  John J Reilly,et al.  Objective measurement of posture and posture transitions in the pre-school child , 2012, Physiological measurement.

[3]  Kamiar Aminian,et al.  Ambulatory Monitoring of Physical Activities in Patients With Parkinson's Disease , 2007, IEEE Transactions on Biomedical Engineering.

[4]  Hikaru Inooka,et al.  Classification of human moving patterns using air pressure and acceleration , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[5]  John Nelson,et al.  The measurement of sedentary patterns and behaviors using the activPAL™ Professional physical activity monitor , 2012, Physiological measurement.

[6]  Roger Gassert,et al.  Low-power sensor module for long-term activity monitoring , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[8]  K Aminian,et al.  Multi-parametric evaluation of sit-to-stand and stand-to-sit transitions in elderly people. , 2011, Medical engineering & physics.

[9]  Wim G. M. Janssen,et al.  Determinants of the sit-to-stand movement: a review. , 2002, Physical therapy.

[10]  Lucas J Carr,et al.  Letter to the editor: standardized use of the terms "sedentary" and "sedentary behaviours". , 2012, Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme.

[11]  M. Tremblay,et al.  A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review , 2008, The international journal of behavioral nutrition and physical activity.

[12]  L. Pogliani,et al.  On the barometric formula , 1997 .

[13]  J. B. J. Bussmann,et al.  Measuring daily behavior using ambulatory accelerometry: The Activity Monitor , 2001, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[14]  A. Paraschiv-lonescu,et al.  Detection and Classification of Postural Transitions in Real-World Conditions , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  R. Huston Principles of Biomechanics , 2008 .

[16]  K Aminian,et al.  Ambulatory system for the quantitative and qualitative analysis of gait and posture in chronic pain patients treated with spinal cord stimulation. , 2004, Gait & posture.

[17]  M. Tremblay,et al.  Reply to the Discussion of "Letter to the Editor: Standardized use of the terms sedentary and sedentary behaviours" — Sitting and reclining are different states , 2012 .

[18]  Martin Däumer,et al.  A new method to estimate the real upper limit of the false alarm rate in a 3 accelerometry-based fall detector for the elderly , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Ning Wang,et al.  Energy expenditure estimation during normal ambulation using triaxial accelerometry and barometric pressure , 2012, Physiological measurement.

[20]  S. Chastin,et al.  Methods for objective measure, quantification and analysis of sedentary behaviour and inactivity. , 2010, Gait & posture.

[21]  Panagiota Anastasopoulou,et al.  Classification of human physical activity and energy expenditure estimation by accelerometry and barometry , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  Hikaru Inooka,et al.  Automatic classification of ambulatory movements and evaluation of energy consumptions utilizing accelerometers and a barometer , 2005 .

[23]  T. Fujita,et al.  Wireless MEMS Sensing System for Human Activity Monitoring , 2007, 2007 IEEE/ICME International Conference on Complex Medical Engineering.

[24]  Philipp Scholl,et al.  jNode: A sensor network platform that supports distributed inertial kinematic monitoring , 2012, 2012 Ninth International Conference on Networked Sensing (INSS).

[25]  S. Cerutti,et al.  Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.