Can smartwatches replace smartphones for posture tracking?

Sensors (Switzerland)

Volumn 15, Issue 10, 2015, Pages 26783-26800

  Mortazavi, Bobak ^a   Nemati, Ebrahim ^b   Wall, Kristina Vander ^b   Flores Rodriguez, Hector G ^c   Cai, Jun Yu Jacinta ^d   Lucier, Jessica ^b   Naeim, Arash ^b   Sarrafzadeh, Majid ^a,b  

^a University of California   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c El Camino College   (United States)

^d UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

Author keywords

Activity recognition; Embedded medical systems; Machine learning; Posture tracking; Smartwatch; Wireless health

Indexed keywords

EMBEDDED SYSTEMS; LEARNING SYSTEMS; MHEALTH; SMARTPHONES;

ACTIVITY RECOGNITION; MEDICAL SYSTEMS; POSTURE TRACKING; SMARTWATCH; WIRELESS HEALTHS;

WEARABLE COMPUTERS;

ADULT; AMBULATORY MONITORING; BODY POSTURE; CLOTHING; DEVICES; HUMAN; PHYSIOLOGY; PROCEDURES; SMARTPHONE; TELEMEDICINE; YOUNG ADULT;

ADULT; CLOTHING; HUMANS; MONITORING, AMBULATORY; POSTURE; SMARTPHONE; TELEMEDICINE; YOUNG ADULT;

EID: 84948182343     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s151026783     Document Type: Article

References (41)

1. Dey, A.K.; Wac, K.; Ferreira, D.; Tassini, K.; Hong, J.H.; Ramos, J. Getting closer: An empirical investigation of the proximity of user to their smart phones. In Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China, 17–21 September 2011; ACM: New York, NY, USA, 2011; pp. 163–172.
2. Lan, M.; Samy, L.; Alshurafa, N.; Suh, M.K.; Ghasemzadeh, H.; Macabasco-O’Connell, A.; Sarrafzadeh, M. Wanda: An end-to-end remote health monitoring and analytics system for heart failure patients. In Proceedings of the Conference on Wireless Health, La Jolla, CA, USA, 22–25 October 2012; ACM: New York, NY, USA, 2012.
3. Alshurafa, N.; Eastwood, J.; Nyamathi, S.; Xu, W.; Liu, J.J.; Sarrafzadeh, M. Battery Optimization in Smartphones for Remote Health Monitoring Systems to Enhance User Adherence. In Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments, Island of Rhodes, Greece, 27–30 May 2014; ACM: New York, NY, USA, 2014.
4. Rawassizadeh, R.; Tomitsch, M.; Wac, K.; Tjoa, A.M. UbiqLog: A generic mobile phone-based life-log framework. Pers. Ubiquitous Comput. 2013, 17, 621–637.
5. Van Laerhoven, K.; Borazio, M.; Burdinski, J.H. Wear is Your Mobile? Investigating Phone Carrying and Use Habits with aWearable Device. Front. ICT 2015, 2, doi:10.3389/fict.2015.00010.
6. Roggen, D.; Perez, D.G.; Fukumoto, M.; Van Laerhoven, K. ISWC 2013—Wearables Are Here to Stay. IEEE Pervasive Comput. 2014, 13, 14–18.
7. Rawassizadeh, R.; Price, B.A.; Petre, M. Wearables: Has the age of smartwatches finally arrived? Commun. ACM 2015, 58, 45–47.
8. Tognetti, A.; Lorussi, F.; Tesconi, M.; Bartalesi, R.; Zupone, G.; de Rossi, D. Wearable kinesthetic systems for capturing and classifying body posture and gesture. In Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, Shanghai, China, 17–18 January 2006; pp. 1012–1015.
9. Dietrich, M.; van Laerhoven, K. A Typology of Wearable Activity Recognition and Interaction. In Proceedings of the 2nd International Workshop on Sensor-Based Activity Recognition and Interaction, Rostock, Germany, 25–26 June 2015; ACM: New York, NY, USA, 2015.
10. Mayer, S.; Soros, G. User Interface Beaming–Seamless Interaction with Smart Things Using Personal Wearable Computers. In Proceedings of the 11th International Conference on Wearable and Implantable Body Sensor Networks Workshops (BSN Workshops), Zurich, Switzerland, 16–19 June 2014; pp. 46–49.
11. Mortazavi, B.J.; Pourhomayoun, M.; Alsheikh, G.; Alshurafa, N.; Lee, S.I.; Sarrafzadeh, M. Determining the Single Best Axis for Exercise Repetition Recognition and Counting on SmartWatches. In Proceedings of the 11th International Conference on Wearable and Implantable Body Sensor Networks, Zurich, Switzerland, 16–19 June 2014; pp. 33–38.
12. Zeni, M.; Zaihrayeu, I.; Giunchiglia, F. Multi-device activity logging. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, Seattle, WA, USA, 13–17 September 2014; ACM: New York, NY, USA, 2014; pp. 299–302.
13. Bieber, G.; Kirste, T.; Urban, B. Ambient Interaction by Smart Watches. In Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments, Heraklion, Crete, Greece, 6–9 June 2012; ACM: New York, NY, USA, 2012.
14. Bieber, G.; Haescher, M.; Vahl, M. Sensor Requirements for Activity Recognition on Smart Watches. In Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments, Island of Rhodes, Greece, 29–31 May 2013; ACM: New York, NY, USA, 2013.
15. Ghasemzadeh, H.; Ostadabbas, S.; Guenterberg, E.; Pantelopoulos, A. Wireless Medical-Embedded Systems: A Review of Signal-Processing Techniques for Classification. IEEE Sens. J. 2013, 13, 423–437.
16. Bao, L.; Intille, S.S. Activity recognition from user-annotated acceleration data. In Pervasive Computing; Springer: Berlin, Germany, 2004; pp. 1–17.
17. Ravi, N.; Dandekar, N.; Mysore, P.; Littman, M.L. Activity recognition from accelerometer data. In Proceedings of the Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference, Pittsburgh, PA, USA, 9–13 July 2005; pp. 1541–1546.
18. Chaudhry, S.I.; Phillips, C.O.; Stewart, S.S.; Riegel, B.; Mattera, J.A.; Jerant, A.F.; Krumholz, H.M. Telemonitoring for patients with chronic heart failure: A systematic review. J. Card. Fail. 2007, 13, 56–62.
19. Chaudhry, S.I.; Mattera, J.A.; Curtis, J.P.; Spertus, J.A.; Herrin, J.; Lin, Z.; Phillips, C.O.; Hodshon, B.V.; Cooper, L.S.; Krumholz, H.M. Telemonitoring in patients with heart failure. N. Engl. J. Med. 2010, 363, 2301–2309.
20. Suh, M.k.; Chen, C.A.; Woodbridge, J.; Tu, M.K.; Kim, J.I.; Nahapetian, A.; Evangelista, L.S.; Sarrafzadeh, M. A remote patient monitoring system for congestive heart failure. J. Med. Syst. 2011, 35, 1165–1179.
21. Alshurafa, N.; Xu, W.; Liu, J.J.; Huang, M.C.; Mortazavi, B.; Sarrafzadeh, M.; Roberts, C.K. Robust human intensity-varying activity recognition using Stochastic Approximation in wearable sensors. In Proceedings of the 2013 IEEE International Conference on Body Sensor Networks (BSN), Cambridge, MA, USA, 6–9 May 2013; pp. 1–6.
22. Pourhomayoun, M.; Jin, Z.; Fowler, M.L. Indoor Localization, Tracking and Fall Detection for Assistive Healthcare Based on Spatial Sparsity and Wireless Sensor Network. Int. J. Monit. Surveill. Technol. Res. 2013, 1, 72–83.
23. Wiens, J.; Guttag, J.; Horvitz, E. Learning evolving patient risk processes for c. diff colonization. ICML Workshop on Machine Learning from Clinical Data, Edinburgh, UK, 30 June–1 July 2012.
24. Lee, S.I.; Ghasemzadeh, H.; Mortazavi, B.; Lan, M.; Alshurafa, N.; Ong, M.; Sarrafzadeh, M. Remote Patient Monitoring: What Impact Can Data Analytics Have on Cost? In Proceedings of the 4th Conference on Wireless Health, Baltimore, MD, USA, 1–3 November 2013; ACM: New York, NY, USA, 2013.
25. Aaldriks, A.; Maartense, E.; Le Cessie, S.; Giltay, E.; Verlaan, H.; van der Geest, L.; Kloosterman-Boele, W.; Peters-Dijkshoorn, M.; Blansjaar, B.; Van Schaick, H. et al. Predictive value of geriatric assessment for patients older than 70 years, treated with chemotherapy. Crit. Rev. Oncol. Hematol. 2011, 79, 205–212.
26. Balan, R.K.; Lee, Y.; Wee, T.K.; Misra, A. The challenge of continuous mobile context sensing. In Proceedings of the 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS), Bangalore, India, 6–10 January 2014; pp. 1–8.
27. Alshurafa, N.; Eastwood, J.A.; Pourhomayoun, M.; Nyamathi, S.; Bao, L.; Mortazavi, B.; Sarrafzadeh, M. Anti-Cheating: Detecting Self-Inflicted and Impersonator Cheaters for Remote Health Monitoring Systems with Wearable Sensors. In Proceedings of the 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Zurich, Switzerland, 16–19 June 2014; pp. 92–97.
28. Shih, P.C.; Han, K.; Poole, E.S.; Rosson, M.B.; Carroll, J.M. Use and adoption challenges of wearable activity trackers. Available online: http://hdl.handle.net/2142/73649 (accessed on 21 October 2015)
29. Chowdhury, D.; Banerjee, S.J.; Sanyal, K.; Chattopadhyay, M. A real time gesture recognition with wrist mounted accelerometer. In Information Systems Design and Intelligent Applications; Springer: Berlin, Germany, 2015; pp. 245–253.
30. Ugulino, W.; Cardador, D.; Vega, K.; Velloso, E.; Milidiú, R.; Fuks, H. Wearable computing: Accelerometers’ data classification of body postures and movements. In Advances in Artificial Intelligence—SBIA 2012; Springer: Berlin, Germany, 2012; pp. 52–61.
31. Shin, D.M.; Shin, D.; Shin, D. Smart watch and monitoring system for dementia patients. In Grid and Pervasive Computing; Springer: Berlin, Germany, 2013; pp. 577–584.
32. Park, C.; Kim, J.; Choi, H.J. A watch-type human activity detector for the aged care. In Proceedings of the 14th International Conference on Advanced Communication Technology (ICACT), PyeongChang, Korea, 19–22 February 2012; pp. 648–652.
33. Rawassizadeh, R.; Tomitsch, M.; Nourizadeh, M.; Momeni, E.; Peery, A.; Ulanova, L.; Pazzani, M. Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches. Sensors 2015, 15, 22616–22645.
34. Zhang, M.; Sawchuk, A.A. A feature selection-based framework for human activity recognition using wearable multimodal sensors. In Proceedings of the 6th International Conference on Body Area Networks, Beijng, China, 7–10 November 2011; pp. 92–98.
35. Yang, J.Y.; Wang, J.S.; Chen, Y.P. Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers. Pattern Recognit. Lett. 2008, 29, 2213–2220.
36. Vaha-Ypya, H.; Vasankari, T.; Husu, P.; Suni, J.; Sievanen, H. A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. Clin. Physiol. Funct. Imaging 2015, 35, 64–70.
37. Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.H. The WEKA data mining software: An update. ACM SIGKDD Explor. Newslett. 2009, 11, 10–18.
38. Prichep, L.S.; Jacquin, A.; Filipenko, J.; Dastidar, S.G.; Zabele, S.; Vodencarevic, A.; Rothman, N.S. Classification of traumatic brain injury severity using informed data reduction in a series of binary classifier algorithms. IEEE Trans. Neural Syst. Rehabil. Eng. 2012, 20, 806–822.
39. Üstün, B.; Melssen, W.J.; Buydens, L.M. Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel. Chemom. Intell. Lab. Syst. 2006, 81, 29–40.
40. Mortazavi, B.; Pourhomayoun, M.; Nyamathi, S.; Wu, B.; Lee, S.I.; Sarrafzadeh, M. Multiple model recognition for near-realistic exergaming. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom), St. Louis, MO, USA, 23–27 March 2015; pp. 140–148.
41. Sokolova, M.; Lapalme, G. A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 2009, 45, 427–437.