Recognition of Human Activities via Wearable Sensors: Variables Identified in a Systematic Mapping
Authors: 
Luciana De Nardin, 
Kamila R. H. Rodrigues, Larissa C. Zimmermann, Brunela D. M. Orlandi, Maria da Graça C. PimentelAuthors Info & Claims
Luciana De Nardin, Kamila R. H. Rodrigues, Larissa C. Zimmermann, Brunela D. M. Orlandi, Maria da Graça C. PimentelAuthors Info & ClaimsPages 49 - 56
Abstract
Worldwide, the composition of the population registers an increase in life expectancy and a reduction in the birth rate. The creative use of technology and the omnipresence of sensor-based devices allow the development of software solutions and platforms that offer services to older people towards increasing quality of life, reducing expenses, and promoting aging in place while preserving autonomy and security: a case in point is wearable sensor-based Human Activity Recognition (HAR). Given that previous work observed the lack of attention given to older people by the researchers investigating HAR systems, we conducted a systematic mapping aiming at identifying resources, as variables and associated values, used in the development of HAR systems for older people. The systematic mapping selected 21 papers published between 2010 and 2019, which exposed the use of 16 variables and associated values.
References
[1]
Stefano Abbate, Marco Avvenuti, Francesco Bonatesta, Guglielmo Cola, Paolo Corsini, and Alessio Vecchio. 2012. A smartphone-based fall detection system. Pervasive and Mobile Computing 8, 6 (2012), 883--899.
[2]
Helmut Brath, Jürgen Morak, Thomas Kästenbauer, Robert Modre-Osprian, Hermine Strohner-Kästenbauer, Mark Schwarz, Willem Kort, and Günter Schreier. 2013. Mobile health (mHealth) based medication adherence measurement--a pilot trial using electronic blisters in diabetes patients. British Journal of Clinical Pharmacology 76 (2013), 47--55.
[3]
Nicole A Capela, Edward D Lemaire, and Natalie Baddour. 2015. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients. PLoS One 10, 4 (2015), e0124414.
[4]
Josip Car, Woan Shin Tan, Zhilian Huang, Peter Sloot, and Bryony Dean Franklin. 2017. eHealth in the future of medications management: personalisation, monitoring and adherence. BMC Medicine 15, 1 (2017), 73.
[5]
Guo-Jing Chan, Dong-Hung Lin, Chih-Wei Yi, and Chien-Chao Tseng. 2016. A two-layer hierarchical framework for activity sequence recognition by wearable sensors. In Asia-Pacific Network Operations and Management Symp. IEEE, 1--4.
[6]
Josh Cherian, Vijay Rajanna, Daniel Goldberg, and Tracy Hammond. 2017. Did you remember to brush? a noninvasive wearable approach to recognizing brushing teeth for elderly care. In Proc. Pervasive Health. 48--57.
[7]
Saisakul Chernbumroong, Shuang Cang, Anthony Atkins, and Hongnian Yu. 2013. Elderly activities recognition and classification for applications in assisted living. Expert Systems with Applications 40, 5 (2013), 1662--1674.
[8]
Saisakul Chernbumroong, Shuang Cang, and Hongnian Yu. 2014. A practical multi-sensor activity recognition system for home-based care. Decision Support Systems 66 (2014), 61--70.
[9]
Seunghyun Choi and Sekyoung Youm. 2019. A study on a fall detection monitoring system for falling elderly using open source hardware. Multimedia Tools and Applications (2019), 1--12.
[10]
Matteo Ciman, Michele Donini, Ombretta Gaggi, and Fabio Aiolli. 2016. Stairstep recognition and counting in a serious Game for increasing users' physical activity. Personal and Ubiquitous Computing 20, 6 (2016), 1015--1033.
[11]
Theodore D Cosco, Joseph Firth, Ipsit Vahia, Andrew Sixsmith, and John Torous. 2019. Mobilizing mhealth data collection in older adults: challenges and opportunities. JMIR aging 2, 1 (2019), e10019.
[12]
David Crary. 2011. Aging in place. Most seniors want to stay put. http://www.nbcnews.com/id/45376299/ns/health-aging/t/aging-place-most-seniors-want-stay-put/
[13]
Bruna CR Cunha. 2019. ESPIM: um modelo para guiar o desenvolvimento de sistemas de intervenção a distância. Ph.D. Dissertation. Universidade de São Paulo. https://www.teses.usp.br/teses/disponiveis/55/55134/tde-29082019-090151
[14]
Asbjørn Danielsen, Hans Olofsen, and Bernt Arild Bremdal. 2016. Increasing fall risk awareness using wearables: a fall risk awareness protocol. Journal of Biomedical Informatics 63 (2016), 184--194.
[15]
Kadian Davis, Evans Owusu, Vahid Bastani, Lucio Marcenaro, Jun Hu, Carlo Regazzoni, and Loe Feijs. 2016. Activity recognition based on inertial sensors for ambient assisted living. In FUSION'16. IEEE, 371--378.
[16]
Miguel Ángel Álvarez de la Concepción, Luis Miguel Soria Morillo, Juan Antonio Álvarez García, and Luis González-Abril. 2017. Mobile activity recognition and fall detection system for elderly people using Ameva algorithm. Pervasive and Mobile Computing 34 (2017), 3--13.
[17]
Rafael De Pinho André, Pedro Diniz, and Hugo Fuks. 2017. Bottom-up investigation: Human activity recognition based on feet movement and posture information. In Intl. Work. Sensor-based Activity Recognition and Interaction. 1--6.
[18]
Alejandro Galán-Mercant, Andrés Ortiz, Enrique Herrera-Viedma, Maria Teresa Tomas, Beatriz Fernandes, and Jose A Moral-Munoz. 2019. Assessing physical activity and functional fitness level using convolutional neural networks. Knowledge-Based Systems 185 (2019), 104939.
[19]
Lei Gao, AK Bourke, and John Nelson. 2014. Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. Medical Engineering & Physics 36, 6 (2014), 779--785.
[20]
Global Age Watch Index. 2014. Insight Report, London, UK: HelpAge Intl.
[21]
Giuliano Grossi, Raffaella Lanzarotti, Paolo Napoletano, Nicoletta Noceti, and Francesca Odone. 2019. Positive technology for elderly well-being: A review. Pattern Recognition Letters (2019).
[22]
Marian Haescher, Denys JC Matthies, Karthik Srinivasan, and Gerald Bieber. 2018. Mobile assisted living: smartwatch-based fall risk assessment for elderly people. In Proc. Intl. Workshop on Sensor-based Activity Recognition and Interaction. 1--10.
[23]
Moha mmed Hassan, Md Zia Uddin, Amr Mohamed, and Ahmad Almogren. 2018. A robust human activity recognition system using smartphone sensors and deep learning. Future Generation Computer Systems 81 (2018), 307--313.
[24]
B Hu, PC Dixon, JV Jacobs, JT Dennerlein, and JM Schiffman. 2018. Machine learning algorithms based on signals from a single wearable inertial sensor can detect surface-and age-related differences in walking. Journal of Biomechanics 71 (2018), 37--42.
[25]
Guibing Hu, Xuesong Qiu, and Luoming Meng. 2017. Human activity recognition based on hidden Markov models using computational RFID. In Intl. Conf. on Systems and Informatics (ICSAI). IEEE, 813--818.
[26]
Faisal Hussain, Muhammad Ehatisham-ul Haq, Muhammad Awais Azam, and Asra Khalid. 2018. Elderly Assistance Using Wearable Sensors by Detecting Fall and Recognizing Fall Patterns. In Proc. the 2018 ACM Intl. Joint Conf. and 2018 Intl. Symp.Pervasive and Ubiquitous Computing and Wearable Computers. 770--777.
[27]
IBGE. 2019. Projeção da população do Brasil e das Unidades da Federação. https://www.ibge.gov.br/apps/populacao/projecao/index.html
[28]
Bharati Kaudki and Anil Surve. 2018. Human Fall Detection Using RFID Technology. In ICCCNT'18). IEEE, 1--5.
[29]
Heejung Kim, SungHee Lee, SangEun Lee, Soyun Hong, HeeJae Kang, and Namhee Kim. 2019. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone. JMIR mHealth and uHealth 7, 10 (2019), e14149.
[30]
Barbara Kitchenham. 2004. Procedures for performing systematic reviews. Keele, UK, Keele University 33, 2004 (2004), 1--26.
[31]
Martin Kodyš, Pau Oliver, Joaquim Bellmunt, and Mounir Mokhtari. 2017. Human urban mobility classification in AAL deployments using mobile devices. In Proc. Intl. Conf. Information Integration and Web-based Applications & Services. 311--319.
[32]
Oscar D Lara and Miguel A Labrador. 2012. A survey on human activity recognition using wearable sensors. IEEE Communications Surveys & Tutorials 15, 3 (2012), 1192--1209.
[33]
Isah A Lawal and Sophia Bano. 2019. Deep human activity recognition using wearable sensors. In Proc. ACM Intl. Conf. Pervasive Technologies Related to Assistive Environments. 45--48.
[34]
Juarez Monteiro, Roger Granada, Rodrigo C Barros, and Felipe Meneguzzi. 2017. Deep neural networks for kitchen activity recognition. In 2017 Intl. Joint Conf. on Neural Networks (IJCNN). IEEE, 2048-2055.
[35]
Ku Nurhanim, Irraivan Elamvazuthi, LI Izhar, and T Ganesan. 2017. Classification of human activity based on smartphone inertial sensor using support vector machine. In IEEE Intl. Symp. in Robotics and Manufacturing Automation. 1--5.
[36]
Megan K O'Brien, Nicholas Shawen, Chaithanya K Mummidisetty, Saninder Kaur, Xiao Bo, Christian Poellabauer, Konrad Kording, and Arun Jayaraman. 2017. Activity recognition for persons with stroke using mobile phone technology: toward improved performance in a home setting. JMIR - Journal of Medical Internet Research 19, 5 (2017), e184.
[37]
Organização das Nações Unidas. 2019. World Population Prospects 2019: Highlights (ST/ESA/SER.A/423). https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdf
[38]
Moacir Ponti, Patricia Bet, Caroline L Oliveira, and Paula C Castro. 2017. Better than counting seconds: Identifying fallers among healthy elderly using fusion of accelerometer features and dual-task Timed Up and Go. PLoS One 12, 4 (2017), e0175559.
[39]
Kimberly Ryan Powell, Gregory Lynn Alexander, Richard Madsen, and Chelsea Deroche. 2019. A national assessment of access to technology among nursing home residents: A secondary analysis. JMIR aging 2, 1 (2019), e11449.
[40]
Daniele Riboni, Claudio Bettini, Gabriele Civitarese, Zaffar Haider Janjua, and Rim Helaoui. 2016. Smartfaber: Recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment. Artificial Intelligence in Medicine 67 (2016), 57--74.
[41]
Thiago Augusto Hernandes Rocha, Luiz Augusto Fachini, Elaine Thumé, Núbia Cristina da Silva, Allan Claudius Queiroz Barbosa, Maria do Carmo, and Júnia Marçal Rodrigues. 2016. Saúde Móvel: novas perspectivas para a oferta de serviços em saúde. Epidemiologia e Serviços de Saúde 25 (2016), 159--170.
[42]
Jeffer E Sasaki, Amanda Hickey, John Staudenmayer, Dinesh John, Jane A Kent, and Patty S Freedson. 2016. Performance of activity classification algorithms in free-living older adults. Medicine and Science In Sports and Exercise 48, 5 (2016), 941.
[43]
Nicholas Shawen, Luca Lonini, Chaithanya Krishna Mummidisetty, Ilona Shparii, Mark V Albert, Konrad Kording, and Arun Jayaraman. 2017. Fall detection in individuals with lower limb amputations using mobile phones: Machine learning enhances robustness for real-world applications. JMIR mHealth and uHealth 5, 10 (2017), e151.
[44]
Valeria Soto-Mendoza, J Antonio García-Macías, Edgar Chávez, Jorge R Gomez-Montalvo, and Eduardo Quintana. 2017. Detecting abnormal behaviours of institutionalized older adults through a hybrid-inference approach. Pervasive and Mobile Computing 40 (2017), 708--723.
[45]
Reto A Stucki, Prabitha Urwyler, Luca Rampa, René Müri, Urs P Mosimann, and Tobias Nef. 2014. A web-based non-intrusive ambient system to measure and classify activities of daily living. J Med Internet Res 16, 7 (2014), e175.
[46]
Liyakathunisa Syed, Saima Jabeen, S Manimala, and Abdullah Alsaeedi. 2019. Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques. Future Generation Computer Systems (2019).
[47]
Lei Tang, Xingshe Zhou, Zhiwen Yu, Yunji Liang, Daqing Zhang, and Hongbo Ni. 2011. MHS: A multimedia system for improving medication adherence in elderly care. IEEE Systems Journal 5, 4 (2011), 506--517.
[48]
Theodora Toutountzi, Chris Collander, Scott Phan, and Fillia Makedon. 2016. Eyeon: An activity recognition system using myo armband. In Proc. ACM Intl. Conf. on Pervasive Technologies Related to Assistive Environments. 1--3.
[49]
Yan Wang, Shuang Cang, and Hongnian Yu. 2019. A survey on wearable sensor modality centred human activity recognition in health care. Expert Systems with Applications (2019).
[50]
Aner Weiss, Marina Brozgol, Nir Giladi, and Jeffrey M Hausdorff. 2016. Can a single lower trunk body-fixed sensor differentiate between level-walking and stair descent and ascent in older adults? Preliminary findings. Medical Engineering &Physics 38, 10 (2016), 1146--1151.
[51]
Janine L Wiles, Annette Leibing, Nancy Guberman, Jeanne Reeve, and Ruth ES Allen. 2012. The meaning of "aging in place" to older people. The Gerontologist 52, 3 (2012), 357--366.
[52]
Johann P Wolff, Florian Grützmacher, Arne Wellnitz, and Christian Haubelt. 2018. Activity Recognition using Head Worn Inertial Sensors. In Proc. Intl. Workshop on Sensor-based Activity Recognition and Interaction. 1--7.
[53]
World Health Organization. 2011. mHealth: new horizons for health through mobile technologies. https://www.who.int/goe/publications/goe_mhealth_web.pdf
[54]
Cheng Xu, Duo Chai, Jie He, Xiaotong Zhang, and Shihong Duan. 2019. InnoHAR: a deep neural network for complex human activity recognition. IEEE Access 7 (2019), 9893--9902.
[55]
Zimin Xu, Guoli Wang, and Xuemei Guo. 2019. Comparative Studies on Activity Recognition of Elderly People Living Alone. In Chinese Intelligent Systems Conf. Springer, 276--291.
[56]
Yang Yang, John P Hirdes, Joel A Dubin, and Joon Lee. 2019. Fall Risk Classification in Community-Dwelling Older Adults Using a Smart Wrist-Worn Device and the Resident Assessment Instrument-Home Care: Prospective Observational Study. JMIR Aging 2, 1 (2019), e12153.
[57]
Isabela Zaine, David M Frohlich, Kamila RH Rodrigues, Bruna CR Cunha, Alex F Orlando, Leonardo F Scalco, and Maria Da Graça C Pimentel. 2019. Promoting Social Connection and Deepening Relations Among Older Adults: Design and Qualitative Evaluation of Media Parcels. J Med Internet Res 21, 10 (3 Oct 2019), e14112. https://doi.org/10.2196/14112
[58]
Tingzhi Zhao, Hongbo Ni, Xingshe Zhou, Lin Qiang, Daqing Zhang, and Zhiwen Yu. 2014. Detecting Abnormal Patterns of Daily Activities for the Elderly Living Alone. In Health Information Science, Yanchun Zhang, Guiqing Yao, Jing He, Lei Wang, Neil R. Smalheiser, and Xiaoxia Yin (Eds.). Springer, 95--108.
[59]
Larissa C Zimmermann. 2019. Elderly activity recognition using smartphones and wearable devices. https://www.teses.usp.br/teses/disponiveis/55/55134/tde-10062019-080049
[60]
Larissa C Zimmermann, Kamila RH Rodrigues, and Maria da Graça C Pimentel. 2019. EPARS: elderly physical activity reminder system using smartphone and wearable sensors. In Adjunct Proc. 2019 Ubicomp/ISWC. 1139--1145.
Index Terms
- Recognition of Human Activities via Wearable Sensors: Variables Identified in a Systematic Mapping
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