A smartwatch-based framework for real-time and online assessment and mobility monitoring

Smartphone and smartwatch technology is changing the transmission and monitoring landscape for patients and research participants to communicate their healthcare information in real time. Flexible, bidirectional and real-time control of communication allows development of a rich set of healthcare applications that can provide interactivity with the participant and adapt dynamically to their changing environment. Additionally, smartwatches have a variety of sensors suitable for collecting physical activity and location data. The combination of all these features makes it possible to transmit the collected data to a remote server, and thus, to monitor physical activity and potentially social activity in real time. As smartwatches exhibit high user acceptability and increasing popularity, they are ideal devices for monitoring activities for extended periods of time to investigate the physical activity patterns in free-living condition and their relationship with the seemingly random occurring illnesses, which have remained a challenge in the current literature. Therefore, the purpose of this study was to develop a smartwatch-based framework for real-time and online assessment and mobility monitoring (ROAMM). The proposed ROAMM framework will include a smartwatch application and server. The smartwatch application will be used to collect and preprocess data. The server will be used to store and retrieve data, remote monitor, and for other administrative purposes. With the integration of sensor-based and user-reported data collection, the ROAMM framework allows for data visualization and summary statistics in real-time.

[1]  A. Saperstein,et al.  Depressed mood in individuals with schizophrenia: A comparison of retrospective and real-time measures , 2015, Psychiatry Research.

[2]  H. Andrés Neyem,et al.  A cloud-based mobile system to improve respiratory therapy services at home , 2016, J. Biomed. Informatics.

[3]  David A Ganz,et al.  Monitoring Falls in Cohort Studies of Community‐Dwelling Older People: Effect of the Recall Interval , 2005, Journal of the American Geriatrics Society.

[4]  Greg Mori,et al.  A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials , 2016, Medical & Biological Engineering & Computing.

[5]  Gary M. Weiss,et al.  Smartwatch-based activity recognition: A machine learning approach , 2016, 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).

[6]  Sanjay Ranka,et al.  Actigraphy features for predicting mobility disability in older adults , 2016, Physiological measurement.

[7]  F. Stafford,et al.  Calendar and Question-List Survey Methods: Association Between Interviewer Behaviors and Data Quality , 2004 .

[8]  Katarzyna Wac,et al.  Getting closer: an empirical investigation of the proximity of user to their smart phones , 2011, UbiComp '11.

[9]  Michael A. Andrykowski,et al.  Ecological Momentary Assessment of Fatigue Following Breast Cancer Treatment , 2004, Journal of Behavioral Medicine.

[10]  B. Munoz,et al.  Falls and Fear of Falling: Which Comes First? A Longitudinal Prediction Model Suggests Strategies for Primary and Secondary Prevention , 2002, Journal of the American Geriatrics Society.

[11]  S. Cummings,et al.  Forgetting Falls , 1988, Journal of the American Geriatrics Society.

[12]  Aditya Ponnada,et al.  μEMA: Microinteraction-based ecological momentary assessment (EMA) using a smartwatch , 2016, UbiComp.

[13]  Gert R. G. Lanckriet,et al.  Objective Assessment of Physical Activity: Classifiers for Public Health. , 2016, Medicine and science in sports and exercise.

[14]  Parisa Rashidi,et al.  Power-efficient real-time approach to non-wear time detection for smartwatches , 2017, 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[15]  J. Farrar,et al.  Clinical importance of changes in chronic pain intensity measured on an 11-point numerical pain rating scale , 2001, PAIN.

[16]  Nathaniel P. Katz,et al.  Comparative study of electronic vs. paper VAS ratings: a randomized, crossover trial using healthy volunteers , 2002, PAIN.

[17]  G Donaldson,et al.  (121) Self-report ecological momentary assessment in patients with fibromyalgia to examine temporal relationships between pain with mood, fatigue, and sleep. , 2016, The journal of pain : official journal of the American Pain Society.

[18]  S. Shiffman,et al.  Understanding recall of weekly pain from a momentary assessment perspective: absolute agreement, between- and within-person consistency, and judged change in weekly pain , 2004, Pain.

[19]  S. Sen,et al.  Epidemiology of falls and osteoporotic fractures: a systematic review , 2012, ClinicoEconomics and outcomes research : CEOR.

[20]  Majid Sarrafzadeh,et al.  Smartwatch Based Activity Recognition Using Active Learning , 2017, 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).

[21]  Luigi Ferrucci,et al.  Alteration of travel patterns with vision loss from glaucoma and macular degeneration. , 2013, JAMA ophthalmology.

[22]  Patrick J. McGrath,et al.  Comparison of Average Weekly Pain Using Recalled Paper and Momentary Assessment Electronic Diary Reports in Children With Arthritis , 2014, The Clinical journal of pain.

[23]  C. Depp,et al.  A Pilot Study of Mood Ratings Captured by Mobile Phone Versus Paper-and-Pencil Mood Charts in Bipolar Disorder , 2012, Journal of dual diagnosis.

[24]  Lei Gao,et al.  Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. , 2014, Medical engineering & physics.

[25]  Fuzhong Li,et al.  Comparison of tai chi vs. strength training for fall prevention among female cancer survivors: study protocol for the GET FIT trial , 2012, BMC Cancer.

[26]  Toru Nakamura,et al.  Co-Variation of Depressive Mood and Locomotor Dynamics Evaluated by Ecological Momentary Assessment in Healthy Humans , 2013, PloS one.

[27]  Parisa Rashidi,et al.  ROAMM: A software infrastructure for real-time monitoring of personal health , 2016, 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom).

[28]  J L Kelsey,et al.  Methodologic issues in the study of frequent and recurrent health problems. Falls in the elderly. , 1990, Annals of epidemiology.

[29]  Wolff Schlotz,et al.  Tracking daily fatigue fluctuations in multiple sclerosis: ecological momentary assessment provides unique insights , 2017, Journal of Behavioral Medicine.

[30]  Dylan M. Smith,et al.  Ecological measurement of fatigue and fatigability in older adults with osteoarthritis. , 2010, The journals of gerontology. Series A, Biological sciences and medical sciences.

[31]  R. Cumming,et al.  Prospective study of the impact of fear of falling on activities of daily living, SF-36 scores, and nursing home admission. , 2000, The journals of gerontology. Series A, Biological sciences and medical sciences.

[32]  D. Cella,et al.  Reliability and Concurrent Validity of Three Visual-Analogue Mood Scales , 1986, Psychological reports.

[33]  Marco Aiello,et al.  Let's get Physiqual - An intuitive and generic method to combine sensor technology with ecological momentary assessments , 2016, J. Biomed. Informatics.

[34]  Shaghayegh Zihajehzadeh,et al.  Experimental evaluation of regression model-based walking speed estimation using lower body-mounted IMU , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[35]  M. V. D. van den Brink,et al.  The Occurrence of Recall Bias in Pediatric Headache: A Comparison of Questionnaire and Diary Data , 2001, Headache.

[36]  Majid Sarrafzadeh,et al.  HIPAA compliant wireless sensing smartwatch application for the self-management of pediatric asthma , 2016, 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[37]  R. Baumgartner,et al.  Fear of falling and restriction of mobility in elderly fallers. , 1997, Age and ageing.

[38]  Chan-Sik Park,et al.  Smartphone Based Real-Time Location Tracking System for Automatic Risk Alert in Building Project , 2012 .

[39]  N. Peel,et al.  Validating recall of falls by older people. , 2000, Accident; analysis and prevention.

[40]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[41]  Gerard Saucier,et al.  The conceptual link between social desirability and cultural normativity. , 2016, International journal of psychology : Journal international de psychologie.

[42]  Saul Shiffman,et al.  Real-time self-report of momentary states in the natural environment: Computerized ecological momentary assessment. , 2000 .

[43]  Fred Friedberg,et al.  Memory for Fatigue in Chronic Fatigue Syndrome: Relationships to Fatigue Variability, Catastrophizing, and Negative Affect , 2008, Behavioral medicine.

[44]  Scott Shenker,et al.  Spark: Cluster Computing with Working Sets , 2010, HotCloud.

[45]  Sanjay Ranka,et al.  A bag-of-words approach for assessing activities of daily living using wrist accelerometer data , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[46]  M. Folstein,et al.  Reliability, validity, and clinical application of the visual analogue mood scale , 1973, Psychological Medicine.

[47]  Christopher J. James,et al.  Validation of a Commercial Android Smartwatch as an Activity Monitoring Platform , 2018, IEEE Journal of Biomedical and Health Informatics.

[48]  M. Csíkszentmihályi,et al.  Validity and Reliability of the Experience‐Sampling Method , 1987, The Journal of nervous and mental disease.