A framework for harmonizing sensor data to support embedded health assessment

The use of in-home and mobile sensing is likely to be a key component of future care and has recently been studied by many research groups world-wide. Researchers have shown that embedded sensors can be used for health assessment such as early illness detection and the management of chronic health conditions. However, research collaboration and data sharing have been hampered by disparate sets of sensors and data collection methods. To date, there have been no studies to investigate common measures that can be used across multiple sites with different types of sensors, which would facilitate large scale studies and reuse of existing datasets. In this paper, we propose a framework for harmonizing heterogeneous sensor data through an intermediate layer, the Conceptual Sensor, which maps physical measures to clinical space. Examples are included for sleep quality and ambulatory physical function.

[1]  Diane J. Cook,et al.  Activity recognition on streaming sensor data , 2014, Pervasive Mob. Comput..

[2]  Pietro Siciliano,et al.  Supervised machine learning scheme for tri-axial accelerometer-based fall detector , 2013, 2013 IEEE SENSORS.

[3]  Daniel J Buysse,et al.  The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research , 1989, Psychiatry Research.

[4]  L. Ferrucci,et al.  A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. , 1994, Journal of gerontology.

[5]  Liang Liu,et al.  Automatic fall detection based on Doppler radar motion signature , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[6]  Steven J. Miller,et al.  Automated technology to speed recognition of signs of illness in older adults. , 2012, Journal of gerontological nursing.

[7]  Marjorie Skubic,et al.  Quantitative Gait Measurement With Pulse-Doppler Radar for Passive In-Home Gait Assessment , 2014, IEEE Transactions on Biomedical Engineering.

[8]  Diane Podsiadlo,et al.  The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons , 1991, Journal of the American Geriatrics Society.

[9]  Misha Pavel,et al.  Home-based cognitive monitoring using embedded measures of verbal fluency in a computer word game , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Tom Henderson,et al.  Logical sensor systems , 1984, J. Field Robotics.

[11]  M P Lawton,et al.  Environment and other determinants of well-being in older people. , 1983, The Gerontologist.

[12]  A. Stiggelbout,et al.  Systematic evaluation of rating scales for impairment and disability in Parkinson's disease , 2002, Movement disorders : official journal of the Movement Disorder Society.

[13]  James M. Keller,et al.  Comparing Fuzzy, Probabilistic, and Possibilistic Partitions , 2010, IEEE Transactions on Fuzzy Systems.

[14]  Yun Li,et al.  A Microphone Array System for Automatic Fall Detection , 2012, IEEE Transactions on Biomedical Engineering.

[15]  T. Hayes,et al.  One walk a year to 1000 within a year: continuous in-home unobtrusive gait assessment of older adults. , 2012, Gait & posture.

[16]  Marjorie Skubic,et al.  Unobtrusive, Continuous, In-Home Gait Measurement Using the Microsoft Kinect , 2013, IEEE Transactions on Biomedical Engineering.

[17]  Marjorie Skubic,et al.  Fall Detection in Homes of Older Adults Using the Microsoft Kinect , 2015, IEEE Journal of Biomedical and Health Informatics.

[18]  M. Johns,et al.  Sensitivity and specificity of the multiple sleep latency test (MSLT), the maintenance of wakefulness test and the Epworth sleepiness scale: Failure of the MSLT as a gold standard , 2000, Journal of sleep research.

[19]  Richard W. Bohannon Comfortable and maximum walking speed of adults aged 20-79 years: reference values and determinants. , 1997, Age and ageing.

[20]  M. Skubic,et al.  Management of Dementia and Depression Utilizing In- Home Passive Sensor Data. , 2013, Gerontechnology : international journal on the fundamental aspects of technology to serve the ageing society.

[21]  James M. Keller,et al.  Clustering in ordered dissimilarity data , 2009, Int. J. Intell. Syst..

[22]  James M. Keller,et al.  Linguistic summarization of video for fall detection using voxel person and fuzzy logic , 2009, Comput. Vis. Image Underst..