A deep learning approach for pressure ulcer prevention using wearable computing
Tóm tắt
In recent years, statistics have confirmed that the number of elderly people is increasing. Aging always has a strong impact on the health of a human being; from a biological of point view, this process usually leads to several types of diseases mainly due to the impairment of the organism. In such a context, healthcare plays an important role in the healing process, trying to address these problems. One of the consequences of aging is the formation of pressure ulcers (PUs), which have a negative impact on the life quality of patients in the hospital, not only from a healthiness perspective but also psychologically. In this sense, e-health proposes several approaches to deal with this problem, however, these are not always very accurate and capable to prevent issues of this kind efficiently. Moreover, the proposed solutions are usually expensive and invasive. In this paper we were able to collect data coming from inertial sensors with the aim, in line with the Human-centric Computing (HC) paradigm, to design and implement a non-invasive system of wearable sensors for the prevention of PUs through deep learning techniques. In particular, using inertial sensors we are able to estimate the positions of the patients, and send an alert signal when he/she remains in the same position for too long a period of time. To train our system we built a dataset by monitoring the positions of a set of patients during their period of hospitalization, and we show here the results, demonstrating the feasibility of this technique and the level of accuracy we were able to reach, comparing our model with other popular machine learning approaches.
Tài liệu tham khảo
Abadi M et al (2015) TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/. Accessed 3 Dec 2019
Amft O (2018) How wearable computing is shaping digital health. IEEE Pervasive Comput 17(1):92–98. https://doi.org/10.1109/MPRV.2018.011591067
Barsocchi P (2013) Position recognition to support bedsores prevention. IEEE J Biomed Health Inform 17(1):53–59. https://doi.org/10.1109/TITB.2012.2220374
Bruneo D, Distefano S, Longo F, Merlino G, Puliafito A (2018) I/Ocloud: adding an IoT dimension to Cloud infrastructures. Computer 51(1):57–65
Cao C, Liu F, Tan H, Song D, Shu W, Li W, Zhou Y, Bo X, Xie Z (2018) Deep learning and its applications in biomedicine. Genom Proteom Bioinform 16(1):17–32. https://doi.org/10.1016/j.gpb.2017.07.003
Chang M, Yu T, Luo J, Duan K, Tu P, Zhao Y, Nagraj N, Rajiv V, Priebe M, Wood EA, Stachura M (2018) Multimodal sensor system for pressure ulcer wound assessment and care. IEEE Trans Ind Inform 14(3):1186–1196. https://doi.org/10.1109/TII.2017.2782213
Choi S (2016) Understanding people with human activities and social interactions for human-centered computing. Hum-centric Comput Inf Sci 6(1):66:1–66:10. https://doi.org/10.1186/s13673-016-0066-1
Chollet F et al (2015) Keras. https://keras.io. Accessed 5 Dec 2019
Cui Y, Shi G, Liu X, Zhao W, Li Y (2015) Research on data communication between intelligent terminals of medical internet of things. In: 2015 international conference on computer science and applications (CSA), pp 357–359. https://doi.org/10.1109/CSA.2015.39
Dhillon MS, McCombie SA, McCombie DB (2012) Towards the prevention of pressure ulcers with a wearable patient posture monitor based on adaptive accelerometer alignment. In: 2012 annual international conference of the IEEE engineering in medicine and biology society, pp 4513–4516. https://doi.org/10.1109/EMBC.2012.6346970
Díaz C, Garcia-Zapirain B, Castillo C, Sierra-Sosa D, Elmaghraby A, Kim PJ (2017) Simulation and development of a system for the analysis of pressure ulcers. In: 2017 IEEE international symposium on signal processing and information technology (ISSPIT), pp 453–458. https://doi.org/10.1109/ISSPIT.2017.8388686
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press. http://www.deeplearningbook.org. Accessed 6 Dec 2019
Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660
Hayn D, Falgenhauer M, Morak J, Wipfler K, Willner V, Liebhart W, Schreier G (2015) An ehealth system for pressure ulcer risk assessment based on accelerometer and pressure data. J Sens 2015:106,537:1–106,537:8
Hu F, Xie D, Shen S (2013) On the application of the internet of things in the field of medical and health care. In: 2013 IEEE international conference on green computing and communications and IEEE Internet of Things and IEEE cyber, physical and social computing, pp 2053–2058. https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.384
Huang J, Lin S, Wang N, Dai G, Xie Y, Zhou J (2020) TSE-CNN: a two-stage end-to-end cnn for human activity recognition. IEEE J Biomed Health Inform 24(1):292–299. https://doi.org/10.1109/JBHI.2019.2909688
Kaşıkçı M, Aksoy M, Ay E (2018) Investigation of the prevalence of pressure ulcers and patient-related risk factors in hospitals in the province of Erzurum: a cross-sectional study. J Tissue Viability 27(3):135–140. https://doi.org/10.1016/j.jtv.2018.05.001
Longo F, Bruneo D, Distefano S, Merlino G, Puliafito A (2016) Stack4Things: a sensing-and-actuation-as-a-service framework for IoT and Cloud integration. Ann Telecommun. https://doi.org/10.1007/s12243-016-0528-5
Mliki H, Bouhlel F, Hammami M (2020) Human activity recognition from UAV-captured video sequences. Pattern Recogn 100:107140. https://doi.org/10.1016/j.patcog.2019.107140
Nisar H, Malik AR, Asawal M, Cheema HM (2016) An electrical stimulation based therapeutic wearable for pressure ulcer prevention. In: 2016 IEEE EMBS conference on biomedical engineering and sciences (IECBES), pp 411–414. https://doi.org/10.1109/IECBES.2016.7843483
Nweke HF, Teh YW, Mujtaba G, Alo UR, Al-garadi MA (2019) Multi-sensor fusion based on multiple classifier systems for human activity identification. Hum-centric Comput Inf Sci 9(1):34. https://doi.org/10.1186/s13673-019-0194-5
Sen D, McNeill J, Mendelson Y, Dunn R, Hickle K (2018) A new vision for preventing pressure ulcers: wearable wireless devices could help solve a common-and serious-problem. IEEE Pulse 9(6):28–31. https://doi.org/10.1109/MPUL.2018.2869339
Shrestha A, Li H, Fioranelli F, Le Kernec J (2019) Activity recognition with cooperative radar systems at C and K band. J Eng 2019(20):7100–7104. https://doi.org/10.1049/joe.2019.0559
Takano M, Ueno A (2019) Noncontact in-bed measurements of physiological and behavioral signals using an integrated fabric-sheet sensing scheme. IEEE J Biomed Health Inform 23(2):618–630. https://doi.org/10.1109/JBHI.2018.2825020
Tsai C, Li C, Lam RW, Li C, Ho S (2020) Diabetes care in motion: blood glucose estimation using wearable devices. IEEE Consumer Electron Mag 9(1):30–34. https://doi.org/10.1109/MCE.2019.2941461
Wang TY, Chen SL, Huang HC, Kuo SH, Shiu YJ (2011) The development of an intelligent monitoring and caution system for pressure ulcer prevention. In: 2011 international conference on machine learning and cybernetics, vol 2, pp 566–571. https://doi.org/10.1109/ICMLC.2011.6016779
Wåhslén J, Lindh T (2017) Real-time performance management of assisted living services for bluetooth low energy sensor communication. In: 2017 IFIP/IEEE symposium on integrated network and service management (IM), pp 1143–1148. https://doi.org/10.23919/INM.2017.7987452
Yousefi R, Ostadabbas S, Faezipour M, Nourani M, Ng V, Tamil L, Bowling A, Behan D, Pompeo M (2011) A smart bed platform for monitoring amp; ulcer prevention. In: 2011 4th international conference on biomedical engineering and informatics (BMEI), vol 3, pp 1362–1366. https://doi.org/10.1109/BMEI.2011.6098589
Zhong Z, Li Y (2016) A recommender system for healthcare based on human-centric modeling. In: 2016 IEEE 13th international conference on e-business engineering (ICEBE), pp 282–286. https://doi.org/10.1109/ICEBE.2016.055
Zhu J, San-Segundo R, Pardo JM (2017) Feature extraction for robust physical activity recognition. Hum-centric Comput Inf Sci 7(1):16. https://doi.org/10.1186/s13673-017-0097-2