Wearable Wireless Sensors for Measuring Calorie Consumption

Faranak Fotouhi-Ghazvini, Saedeh Abbaspour

DOI: 10.4103/jmss.JMSS_15_18

Abstract


Background: The tracking devices could help measuring the heart rate and energy expenditure and recognizing the user's activity. The calorie measurement is a significant achievement for the fitness tracking and the continuous health monitoring. Methods: In this paper, a combination of an accelerometer and a photoplethysmography (PPG) sensor is implemented to calculate the calories consumed. These sensors were mounted next to each other and then were placed on the ankle and finger by flat cable. The sensed data are transferred via Bluetooth to a smartphone in a serial and real-time manner. An Android App is designed to display the user's health data. The average amount of consumed energy is obtained from the combination of the accelerometer sensor based on the laws of motion and the PPG sensor based on the heart rate data. Results: The designed system is tested on 10 nonathlete males and 10 nonathlete females randomly. By applying the wavelet, the value of the acceleration signal variance was reduced from 3.2 to 0.8. The correlation between PPG and pulse oximeter was 0.9. Moreover, the correlation of the accelerometer and treadmill was 0.9. The root mean square error (RMSE) and the P value of the calorie output from PPG and pulse oximeter are 0.53 and 0.008, respectively. The RMSE and the P value of the calories output from the accelerometer and the treadmill are 0.42 and 0.007, respectively. Conclusion: Our device validity and reliability were good by comparing it with a typical smart band, smart watch, and smartphone available in the market. The combined PPG and the accelerometer sensors were compared with the gold standard, the pulse oximeter, and the treadmill. According to the results, there is no significant difference in the values obtained. Therefore, a mobile system is augmented with the wireless accelerometer and PPG that are connected to a smartphone. The system could be carried out with the user at any time and any place.


Keywords


Accelerometer, activity monitoring, calorie consumption, photoplethysmography, smartphone health application

Full Text:

PDF

References


Adibi S, editor. Mobile Health: A Technology Road Map. Springer Series in Bio/Neuroinformatics. Springer International Publishing Switzerland; 2015. ISBN10: 3319128167. DOI 10.1007/978-3-319-12817-7. Back to cited text no. 1

Pettey C. Wearables Hold the Key to Connected Health Monitoring. Digital Business; 2018. Available from: https://www.gartner.com/smarterwithgartner/wearables-hold-the-key-to-connected-health-monitoring/. [Last accessed on 2018 Jul 22]. Back to cited text no. 2

Fan X, Huang H, Qi S, Luo X, Zeng J, Xie Q, et al. Sensing home: A cost-effective design for smart home via heterogeneous wireless networks. Sensors (Basel) 2015;15:30270-92. Back to cited text no. 3

Sukaphat S. An Applying of Accelerometer in Android Platform for Controlling Weight. Nagoya, Japan: International Conference on Information and Social Science; 2013. p. 294-303. Back to cited text no. 4

Kashi S, Reddy C, Reddy AP. Fit assist step count and calorie estimator using accelerometer. Int J Adv Res Computer Commu Eng 2014;3:7912-7. Back to cited text no. 5

Gusenbauer D, Isert C, and Krösche J. Self-Contained Indoor Positioning on Off-The-Shelf Mobile Devices. Zurich: International Conference on Indoor Positioning and Indoor Navigation; 2010. p. 1-9. Back to cited text no. 6

Hwang P, Chou C, Fang W, Hwang C. Smart Shoes Design with Embedded Monitoring Electronics System for Healthcare and Fitness Applications. Nantou: IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW); 2016. p. 1-2. Back to cited text no. 7

Adi E, Joko P, Yeh C, Chou N, Lee M, Lin AY. Integrated wearable system for monitoring heart rate and step during physical activity. Mob Inf Syst 2016;2016:10. Back to cited text no. 8

Pande A, Zhu J, Das AK, Zeng Y, Mohapatra P, Han JJ. Using smartphone sensors for improving energy expenditure estimation. IEEE J Transl Eng Health Med 2015;3:2700212. Back to cited text no. 9

Pal BS, Hepsiba D. Real-time monitoring of daily calorie expenditure using smart phone. Int J Adv Res Comput Sci Softw Eng 2013;3:268-73. Back to cited text no. 10

Duclos M, Fleury G, Lacomme P, Phan R, Ren L, Rousset S. An acceleration vector variance-based method for energy expenditure estimation in a real-life environment with a smartphone/smartwatch integration. Expert Syst Appl 2016;63:435-49. Back to cited text no. 11

Li M, Kwak KC, Kim YT. Estimation of energy expenditure using a patch-type sensor module with an incremental radial basis function neural network. Sensors (Basel) 2016;16. pii: E1566. Back to cited text no. 12

Altini M, Penders J, Vullers R, Amft O. Estimating energy expenditure using body-worn accelerometers: A comparison of methods, sensors number and positioning. IEEE J Biomed Health Inform 2015;19:219-26. Back to cited text no. 13

Carneiro S, Silva J, Aguiar B, Rocha T. Accelerometer-Based Methods for Energy Expenditure using the Smartphone. Turin: IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings; 2015. p. 151-6. Back to cited text no. 14

Boman M, Sanches P. Sensemaking in intelligent health data analytics. KI Künstliche Intell 2015;29:143-52. Back to cited text no. 15

Duus R., Cooray M, Page N. Agentic technology: The impact of activity trackers on user behavior (an extended abstract). In: Stieler M, editors. Creating Marketing Magic and Innovative Future Marketing Trends. Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Cham: Springer; 2017. p. 315-22. Back to cited text no. 16

Asimina S, Chapizanis D, Karakitsios S, Kontoroupis P, Asimakopoulos DN, Maggos T, et al. Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies. Environ Monit Assess 2018;190:155. Back to cited text no. 17

Camp CM, Hayes LB. Identifying beneficial physical activity during school recess: Utility and feasibility of the fitbit. J Behav Educ 2017;26:394-409. Back to cited text no. 18

Schneider M, Chau L. Validation of the fitbit zip for monitoring physical activity among free-living adolescents. BMC Res Notes 2016;9:448. Back to cited text no. 19

Hamari L, Kullberg T, Ruohonen J, Heinonen OJ, Díaz-Rodríguez N, Lilius J, et al. Physical activity among children: Objective measurements using fitbit one® and ActiGraph. BMC Res Notes 2017;10:161. Back to cited text no. 20

King CE, Sarrafzadeh M. A survey of smartwatches in remote health monitoring. J Healthc Inform Res 2018;2:1-24. Back to cited text no. 21

Boletsis C, McCallum S, Landmark BF. The use of smartwatches for health monitoring in home-based dementia care. In: Zhou J, Salvendy G, editors. Human Aspects of IT for the Aged Population. Design for Everyday Life. Lecture Notes in Computer Science. Vol. 9194. Cham, Springer: Springer; 2015. p. 15-26. Back to cited text no. 22

Bojanovsky V, Byrne S, Kirwan P, Cleland I, Nugent C. Evaluation of fall and seizure detection with smartphone and smartwatch devices. In: Ochoa S, Singh P, Bravo J, editors. Ubiquitous Computing and Ambient Intelligence. Lecture Notes in Computer Science. Vol. 10586. Cham: Springer; 2017. p. 275-86. Back to cited text no. 23

Capodieci A, Budner P, Eirich J, Gloor P, Mainetti L. Dynamically adapting the environment for elderly people through smartwatch-based mood detection. In: Grippa F, Leitão J, Gluesing J, Riopelle K, Gloor P, editors. Collaborative Innovation Networks. Studies on Entrepreneurship, Structural Change and Industrial Dynamics. Cham: Springer; 2018. p. 65-73. Back to cited text no. 24

Fit Bit Official Website; 2018. Available from: https://www.fitbit.com/flex2. [Last accessed on 2018 Aug 18]. Back to cited text no. 25

Garmin Vivofit Official Website; 2018; Available from: https://www.activestride.com.au/garmin-vivofit-replacement-bands. [Last accessed on 2018 Aug 18]. Back to cited text no. 26

Huawei Website; 2018. Available from: https://consumer.huawei.com/us/wearables/watch/. [Last accessed on 2018 Aug 18]. Back to cited text no. 27

Apple Website; 2018. Available from: https://www.apple.com/shop/buy-watch/apple-watch. [Last accessed on 2018 Aug 18]. Back to cited text no. 28

Swain DP, Abernathy KS, Smith CS, Lee SJ, Bunn SA. Target heart rates for the development of cardiorespiratory fitness. Med Sci Sports Exerc 1994;26:112-6. Back to cited text no. 29

Kim JW, Park C. A step stride and heading determination for the pedestrian navigation system. J Glob Positioning Syst 2004;3:273-9. Back to cited text no. 30

The ADXL335 Accelerometer Datasheet. Available from: https://www.sparkfun.com/datasheets/Components/SMD/adxl335.pdf. [Last accessed on 2018 May 04]. Back to cited text no. 31

Chiu CK. An Introduction to Wavelets: Academic Press, Harcourt Brace Jovanovich; 1992. p. 266. Back to cited text no. 32

Daubechies I. Ten Lectures on Wavelets. J Acoust Soc Am 1992. p. 357. Back to cited text no. 33

Hernandez E, Weiss GL. A First Course on Wavelets. Studies in Advance Mathematics: CRC Press Inc.; 1996. p. 489. Back to cited text no. 34

Unser M, Aldroubi A. A review of wavelets in biomedical applications. Proc IEEE 1996;84:626-38. Back to cited text no. 35

Donoho DL. Denoising by soft-thresholdin. IEEE Trans Inf Theory 1995;41:613-27. Back to cited text no. 36

German-Sallo Z, Ciufudean C. Waveform adapted wavelet denoising of ECG signals. Proceedings of the 14th WSEAS International Conference on Mathematical and Computational Methods in Science and Engineering (MACMESE '12). Sliema, Malta; Adv Math Comput Methods 2012. p. 172-5. Back to cited text no. 37

Haar A. On the theory of orthogonal functions systems. Doctorate thesis, University of Gottingen, Germany; 1909. Back to cited text no. 38

Strang G. Wavelets. Am Sci 1992;82:250-5. Back to cited text no. 39

Keytel LR, Goedecke JH, Noakes TD, Hiiloskorpi H, Laukkanen R, van der Merwe L, et al. Prediction of energy expenditure from heart rate monitoring during submaximal exercise. J Sports Sci 2005;23:289-97. Back to cited text no. 40

Ceaser TG. The Estimation of Caloric Expenditure Using Three Triaxial Accelerometers. Ph.D. Dissertation. University of Tennessee; 2012. Back to cited text no. 41

Uri D, Mayette GG. Evaluating a Novel Device for Calorie Reduction: The Bite Counter Study. Open Access Master's Theses; 2016. p. 895. Available from: http://digitalcommons.uri.edu/theses/895. [Last accessed on 2019 Sep 22]. Back to cited text no. 42


Refbacks

  • There are currently no refbacks.


 

  https://e-rasaneh.ir/Certificate/22728

https://e-rasaneh.ir/

ISSN : 2228-7477