Abstract
Sensors in activity based computing enable continuous monitoring of numerous physiological signals when attached to the human body. This finds wide application in areas of activity monitoring, bio-medical rehabilitation, and fitness tracking. Primary challenges in embedded application development for smart wearable include high energy efficiency and user compatibility. Existing algorithms and applications are still unable to fully utilize the true power of the data being collected. They provide lot of descriptive data analytics but lack in predictive analysis. Energy efficiency of computing as predicted by Koomey’s is expected to strike the second law of thermodynamics based on Launder’s Limit within few decades. In this work an energy efficient computing technique for next generation mobile applications is developed. Proposed Artificial Intelligence based energy-efficient embedded algorithm that provide personalized training sequence recommendation in order to achieve desired calorie goals. Suggested training sequence of 6 activities fall under high, medium and low calorie burn with achieved median for 234C:535C:688C respectively. The crux of this implementation is Calorie Matrix Regeneration via state feedback technique using Markov Decision Process (MDP) and Genetic Algorithm (GA). Number of generations required by the GA to reach a suboptimal solution is optimized. While Machine learning algorithms are written in C/C++ for effective embedded implementation, certain computationally expensive modules like MDP and GA are coded in Python with proposed IoT cloud based implementation thereby improving battery efficiency to 12–16 h. This implementation is first of its kind and a step ahead of available state of the art fitness training algorithms/applications.
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References
Smart wearable: reflection & orientation, DG connect services, European Commission, Content & Technology, CNECT, Brussels, 2016
The challenges of wearable electronics, TE Connectivity Limited, 2015
Verma P, Rajnish R, Fatima S (2017) Challenges: wearable computing for internet of things. Int J Sci Res
Molinero AR, Martinez DP et al (2007) Detection of gait parameters, bradykinesia and falls in patients with Parkinson’s disease by using a unique triaxial accelerometer. World Parkinson Congress, Glasgow
Mannini A, Sabatini AM (2010) Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors
Allen FR, Ambikairajah E et al (2006) Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models. Physiol Meas 27:935
Carroll A, Heiser G (2010) An analysis of power consumption in a smartphone. In: USENIXATC proceedings of USENIX conference
Demaine ED, Lynch J, Mirano GJ, Tyagi N (2016) Energy-efficient algorithms. In: Proceedings of the 2016 ACM Conference on Innovations in Theoretical Computer Science (pp 321–332). ACM
Anjum A, Ilyas MU (2013) Activity recognition using smartphone sensors. In: Consumer communication and networking conference, IEEE
Ronao CA, Cho SB (2016) Human activity recognition with smart phone sensors using deep learning neural networks. ESA, Elsevier
Capela NA, Lemaire ED et al (2016) Evaluation of a smart phone human activity recognition application with able-bodied and stroke participants. J NeuroEng Rehabil
Anguita D, Ghio A, Oneto L et al (2013) A public domain dataset for human activity recognition using smartphone dataset. In: ESANN
Ravi N, Mysore P et al (2005) Activity recognition from accelerometer data. In: Proceedings of innovative applications of artificial intelligence
Kwapisz JR, Weiss GM, Moore SA (2011) Activity recognition using cell phone accelerometers. SIGKDD Explor Newsl 12(2)
Vasan KK, Surendiran B (2016) Dimensionality reduction using principal component analysis for network intrusion detection. Elsevier
Choudhury T, Consolvo S et al (2008) The mobile sensing platform: an embedded activity recognition system, an Intel research. IEEE
Powell WB (2007) Approximate dynamic programming: solving the curses of dimensionality, vol 703. John Wiley & Sons
Li Y, Cao F (2011) Infinite horizon gradient estimation for semi Markov decision process. In: 8th Asian control conference, IEEE
Rout RR, Krishna MS, Gupta S (2016) Markov decision process-based switching algorithm for sustainable rechargeable wireless sensor networks. IEEE Sens J
Garcia MG, Ruiz J, Ledesma S et al (2010) Combination of acceleration procedures for solving stochastic shortest path Markov decision processes. In: Intelligent systems and knowledge engineering, IEEE
Theodoridis S, Kourtoumbas K (2004) Pattern recognition, 2nd edn, p 582
Gendreau M, Laprte G, Potvin J-Y (1994) Metaheuristics for the vehicle routing problem. Manag Sci 40:1276–1290
Laporte G, Gendreuau M, Potvin J-Y, Semet F (2000) Classical and modern heuristics for the vehicle routing problem. Int Trans Oper Res 7:285–300
Moon S, Bawane N (2015) Optimal feature selection by genetic algorithm for classification using neural network. IRJET. ISSN: 2395-0056
Pei M et al (1995) Genetic algorithm for classification and feature extraction. In: Classification society of North America, USA, 22–25, 1995
Patriarche J, Manduca A, Erickson B (2003) Improved classification accuracy by feature extraction using genetic algorithms. In: Proceedings of SPIE, USA
A deeper lay than Moore’s? The Economist online, web link: https://www.economist.com/blogs/dailychart/2011/10/computing-power
Koomey JG et al (2009) Assessing trends in the electrical efficiency of computation over time. IEEE Ann Hist Comput
Bennett CH (2015) Notes on Landauer’s principle, reversible computation and Maxwell’s demon. Stud Hist Philos Mod Phys
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Singh, J., Mishra, P., Mohapatra, S., Gupta, H.S., Mohapatra, N. (2019). Smart Activity Sequence Generator in Wearable IoT. In: Nath, V., Mandal, J. (eds) Nanoelectronics, Circuits and Communication Systems . Lecture Notes in Electrical Engineering, vol 511. Springer, Singapore. https://doi.org/10.1007/978-981-13-0776-8_32
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DOI: https://doi.org/10.1007/978-981-13-0776-8_32
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