Abstract
A key element of any mechatronics system is in its interaction with the environment within which it operates, and as such sensors and the processing of sensor data play a major role in the operation of such systems. Indeed, in many mechatronics applications from manufacturing to assistive technologies, and increasingly within EcoMechatronics, the role of the embedded sensors is key not only to the operation of an individual device, but also as a source of information impacting upon the wider environment within which that device is operating. In this chapter, the nature of sensing and sensor technology is considered in relation to mechatronic, and particularly EcoMechatronic, applications along with the means by which the resulting data may be analysed and interpreted, illustrated by examples drawn from wearable robotic technologies in particular.
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References
“Eco-design of Energy-Related Products”. European commission's directorate-general for energy. (Wikipedia, accessed Oct. 2021)
Abdul Saboor et al (2020) Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review. IEEE Access Spe Sect Body Area Netw 8
Vallabh P (2018) Reza Malekian. Fall detection monitoring systems: a comprehensive review. J Ambient Intell Human Comput 9:1809–1833
Tsukahara A, Kawanishi R, Hasegawa Y, Sankai Y (2010) Sit-to-stand and stand–to-sit transfer support for complete paraplegic patients with robot suit hal. Adv Robot 24(11):1615–1638
Suzuki K, Mito G, Kawamoto H, Hasegawa Y, Sankai Y (2007) Intention-based walking support for paraplegia patients with robot suit hal. Adv Robot 21(12):1441–1469
Capela NA, Lemaire ED, Baddour N (2015) Improving classification of sit, stand, and lie in a smartphone human activity recognition system. In: Medical measurements and applications (MeMeA), 2015 IEEE international symposium on, IEEE, pp 473–478
Haché G, Lemaire ED, Baddour N (2011) Wearable mobility monitoring using a multimedia smartphone platform. IEEE Trans Instrum Meas 60(9):3153–3161
Luneckas M et al (2021) Hexapod robot gait switching for energy consumption and cost of transport management using heuristic algorithms. Appl Sci 11(3):1339
Favi C et al (2019) A design for disassembly tool oriented to mechatronic product de-manufacturing and recycling. Adv Eng Inf 39:62–79
Noll M-U, Lentz L, von Wagner U (2020) On the improved modeling of the magnetoelastic force in a vibrational energy harvesting system. J Vib Eng Technol 8(2):285–295
Feuz, Dillon K, Cook DJ (2014) Heterogeneous transfer learning for activity recognition using heuristic search techniques Int J Pervas Comput Commun
Chen L, Hoey J, Nugent CD, Cook DJ, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(6):790–808
Kiguchi K, Tanaka T, Fukuda T (2004) Neuro-fuzzy control of a robotic exoskeleton with emg signals. IEEE Trans Fuzzy Syst 12(4):481–490
Banerjee T, Keller JM, Skubic M, Abbott C (2010) Sit-to-stand detection using fuzzy clustering techniques. In: Fuzzy systems (FUZZ), 2010 IEEE international conference on. IEEE, pp 1–8
Rubio-Solis A, Panoutsos G, Beltran-Perez C, Martinez-Hernandez U (2020) A multilayer interval type-2 fuzzy extreme learning machine for the recognition of walking activities and gait events using wearable sensors. Neurocomputing 389:42–55
Doulah A, Shen X, Sazonov E (2016) A method for early detection of the initiation of sit-to-stand posture transitions. Physiol Measur 37(4):515
Qian H, Mao Y, Xiang W, Wang Z (2010) Recognition of human activities using svm multi-class classifier. Pattern Recogn Lett 31(2):100–111
Martinez-Hernandez U, Dehghani-Sanij AA (2019) Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor. Pattern Recognit Lett 118:32–41
Zhao H, Wang Z, Qiu S, Wang J, Xu F, Wang Z, Shen Y (2019) Adaptive gait detection based on foot-mounted inertial sensors and multi-sensor fusion. Inf Fusion 52:157–166
Anania G, Tognetti A, Carbonaro N, Tesconi M, Cutolo F, Zupone G, De Rossi D (2008) Development of a novel algorithm for human fall detection using wearable sensors. In: Sensors, 2008 IEEE. IEEE, pp 1336–1339
He J, Bai S, Wang X (2017) An unobtrusive fall detection and alerting system based on Kalman filter and Bayes network classifier. Sensors 17(6):1393
Wu JK, Dong L, Xiao W (2007) Real-time physical activity classification and tracking using wearable sensors. In: Information, communications & signal processing, 2007 6th international conference on, IEEE, 2007, pp 1–6
Piyathilaka L, Kodagoda S (2013) Gaussian mixture based HMM for human daily activity recognition using 3D skeleton features. In: 2013 IEEE 8th conference on industrial electronics and applications (ICIEA), pp 567–572. IEEE
Grau JB et al (2009) Sustainable agriculture using an intelligent mechatronic system. 2009 35th annual conference of IEEE industrial electronics. IEEE
Li G, Görges D (2018) Ecological adaptive cruise control and energy management strategy for hybrid electric vehicles based on heuristic dynamic programming. IEEE Trans Intell Transp Syst 20(9):3526–3535
Guo J, Xu T (2021) Intelligent low-carbon emission design concept for industrial cities driven by artificial intelligence. 2021 5th international conference on intelligent computing and control systems (ICICCS). IEEE
Hatcher WG, Yu W (2018) A survey of deep learning: platforms, applications and emerging research trends. IEEE Access 6:24411–24432
Nunez JC, Cabido R, Pantrigo JJ, Montemayor AS, Velez JF (2018) Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition. Pattern Recogn 76:80–94
Ijjina EP, Mohan CK (2016) Hybrid deep neural network model for human action recognition. Appl Soft Comput 46:936–952
Ignatov A (2018) Real-time human activity recognition from accelerometer data using convolutional neural networks. Appl Soft Comput 62:915–922
Ordóñez, Javier F, Roggen D (2016) Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1):115
Hammerla NY, Halloran S, Ploetz T (2016) Deep, convolutional, and recurrent models for human activity recognition using wearables. In: Proceedings of the IJCAI international joint conference on artificial intelligence, New York, NY, USA, 9–15 July 2016, vol 2016, pp 1533–1540
Ordóñez FJ, Roggen D (2016) Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16:115
Xing K et al (2018) Hand gesture recognition based on deep learning method. 2018 IEEE third international conference on data science in cyberspace (DSC). IEEE
Al GA, Estrela P, Martinez-Hernandez U (2020) Towards an intuitive human-robot interaction based on hand gesture recognition and proximity sensors. 2020 IEEE international conference on multisensor fusion and integration for intelligent systems (MFI). IEEE
Afolalu, Sunday A et al (2021) Enviable roles of manufacturing processes in sustainable fourth industrial revolution–a case study of mechatronics. Mater Today Proc 44:2895–2901
Ampatzidis Y, De Bellis L, Luvisi A (2017) iPathology: robotic applications and management of plants and plant diseases. Sustainability 9(6):1010
Andronie M et al (2021) Artificial intelligence-based decision-making algorithms, internet of things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems. Electronics 10(20):2497
Male J, Martinez-Hernandez U (2021) Collaborative architecture for human-robot assembly tasks using multimodal sensors. 2021 20th international conference on advanced robotics (ICAR). IEEE
Chen B, Ma H, Qin LY, Gao F, Chan KM, Law SW, Qin L, Liao WH (2016) Recent developments and challenges of lower extremity exoskeletons. J Orthop Transl 5:26–37
Herr H (2009) Exoskeletons and orthoses: classification, design challenges and future directions. J Neuroeng Rehabil 6:1–9
Esquenazi A, Talaty M, Packel A, Saulino M (2012) The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury. Am J Phys Med Rehabil 91:911–921
Barbareschi G, Richards R, Thornton M, Carlson T, Holloway C (2015) Statically vs. dynamically balanced gait: Analysis of a robotic exoskeleton compared with a human. In: Proceedings of the 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Milan, Italy, 25–29 August 2015, pp 6728–6731
Sankai Y (2010) HAL: Hybrid assistive limb based on cybernics. In: Robotics research; Springer: Berlin/Heidelberg, Germany, pp 25–34
Birch N, Graham J, Priestley T, Heywood C, Sakel M, Gall A, Nunn A, Signal N (2017) Results of the first interim analysis of the RAPPER II trial in patients with spinal cord injury: ambulation and functional exercise programs in the REX powered walking aid. J Neuroeng Rehabil 14:1–10
Yan T, Cempini M, Oddo CM, Vitiello N (2015) Review of assistive strategies in powered lower-limb orthoses and exoskeletons. Robot Auton Syst 64:120–136
Sridar S, Qiao Z, Muthukrishnan N, Zhang W, Polygerinos P (2018) A soft-inflatable exosuit for knee rehabilitation: assisting swing phase during walking. Front. Robot. AI 5:44
Park YL, Chen BR, Young D, Stirling L, Wood RJ, Goldfield EC, Nagpal R (2014) Design and control of a bio-inspired soft wearable robotic device for ankle-foot rehabilitation. Bioinspir Biomim 9:016007
Asbeck AT, Schmidt K, Walsh CJ (2015) Soft exosuit for hip assistance. Robot Auton Syst 73:102–110
Asbeck AT, De Rossi SM, Holt KG, Walsh CJ (2015) A biologically inspired soft exosuit for walking assistance. Int J Robot Res 34:744–762
Khomami AM, Najafi F (2021) A survey on soft lower limb cable-driven wearable robots without rigid links and joints. Robot Auton Syst 144:103846
Maqbool HF, Husman MAB, Awad MI, Abouhossein A, Iqbal N, Tahir M, Dehghani-Sanij AA (2018) Heuristic real-time detection of temporal gait events for lower limb amputees. IEEE Sens J 19(8):3138–3148
Martinez-Hernandez U, Dehghani-Sanij AA (2018) Adaptive Bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors. Neural Netw 102:107–119
Liu M, Zhang F, Huang HH (2017) An adaptive classification strategy for reliable locomotion mode recognition. Sensors 17(9):
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The authors would like to thank the postdoctoral and PhD researchers who were involved in the case studies referred to in this chapter. They have been referenced in the references.
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Dehghani-Sanij, A., Martinez-Hernandez, U. (2022). Applied Sensor Technologies. In: Hehenberger, P., Habib, M., Bradley, D. (eds) EcoMechatronics. Springer, Cham. https://doi.org/10.1007/978-3-031-07555-1_6
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DOI: https://doi.org/10.1007/978-3-031-07555-1_6
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