Abstract
Sarcopenia, defined as the progressive loss of mass and muscular function, is an important public health problem with significant economic and social consequences. The implementation of effective preventive and therapeutic interventions is a major challenge due to the increasing number of elderly people suffering from this syndrome and its debilitating complications. The diagnosis of sarcopenia requires the measurement of muscle mass and strength, and physical performance. Each evaluation method has significant limitations in terms of sensitivity and/or specificity. The goal of this work is to develop an integrated technological system, consisting of measuring devices, including mobile and wearable devices, interfacing with a data collection and processing software system, for clinical monitoring and management of the analyzed case studies. The system has been designed to both preventive (early diagnosis) and monitoring purposes of the patient’s condition over time. The diagnosis will support medical personnel in identifying appropriate interventions to prevent or reduce sarcopenia, which can be communicated via apps on smartphones to patients and caregivers, and monitored by medical personnel.
Keywords
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Yu, S.: Sarcopenia in older people. Int. J. Evid. Based Health 12(4), 227–243 (2014)
Shafiee, G., Keshtkar, A., Soltani, A., Ahadi, Z., Larijani, B., Heshmat, R.: Prevalence of sarcopenia in the world: a systematic review and meta-analysis of general population studies. J. Diabetes Metabol. Disord. 16 (2017). Article number: 21. https://doi.org/10.1186/s40200-017-0302-x
Landi, F., et al.: Sarcopenia as a risk factor for falls in elderly individuals: results from the il SIRENTE study. Clin. Nutr. 31(5), 652–658 (2012)
Cederohlm, T., et al.: Sarcopenia and fragility fractures. Eur. J. Phys. Rehabil. Med. 49(1), 111–117 (2013)
Beaudart, C., Zaaria, M., Pasleau, F., Reginster, J.Y., Bruyère, O.: Health outcomes of sarcopenia: a systematic review and meta-analysis. PLoS ONE 12(1), e0169548 (2017)
Han, A., Bokshan, S.L., Marcaccio, S.E., DePasse, J.M., Daniels, A.H.: Diagnostic criteria and clinical outcomes in sarcopenia research: a literature review. J. Clin. Med. 7(4), 70 (2018)
Oakland, K., Nadler, R., Cresswell, L., Jackson, D., Coughlin, P.A.: Systematic review and meta-analysis of the association between frailty and outcome in surgical patients. Ann. Roy. Coll. Surg. Engl. 98(2), 80–85 (2016)
Fess, E.: Grip Strength. Chicago American Society of Hand Therapists, pp. 41–45 (1992)
Norman, K., Otten, L.: Financial impact of sarcopenia or low muscle mass – a short review. Clin. Nutr. 38, 1489–1495 (2019)
Joyce, N.C., Gregory, G.T.: Electrodiagnosis in persons with amyotrophic lateral sclerosis. PM&R J. Injury Funct. Rehabil. 5(5), 89–95 (2013)
Leone, A., Rescio, G., Giampetruzzi, L., Siciliano, P.: Smart EMG-based socks for leg muscles contraction assessment. In: 2019 IEEE International Symposium on Measurements & Networking (M&N), Catania, Italy, pp. 1–6 (2019)
Cruz-Jentoft, A.J., et al.: Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 48, 16–31 (2018)
Beckwée, D., et al.: Exercise interventions for the prevention and treatment of sarcopenia. A systematic umbrella review. J. Nutr. Health Aging 23(6), 494–502 (2019). https://doi.org/10.1007/s12603-019-1196-8
Goel, S.S., Goel, A., Kumar, M., Moltó, G.: A review of Internet of Things: qualifying technologies and boundless horizon. J. Reliable Intell. Environ. 7(1), 23–33 (2021). https://doi.org/10.1007/s40860-020-00127-w
Farao, J., Malila, B., Conrad, N., Mutsvangwa, T., Rangaka, M.X., et al.: A user-centred design framework for mHealth. PLoS ONE 15(8), e0237910 (2020)
https://www.btsbioengineering.com/it/. Accessed 09 June 2021
Rescio, G., Leone, A., Siciliano, P.: Supervised machine learning scheme for electromyography-based pre-fall detection system. Expert Syst. Appl. 100, 95–105 (2018)
Phinyomark, A., Chujit, G., Phukpattaranont, P., Limsakul, C., Huosheng, H.: A preliminary study assessing time-domain EMG features of classifying exercises in preventing falls in the elderly. In: 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), vol. 1, no. 4, pp. 16–18 (2012)
Chorin, F., Cornu, C., Beaune, B., Frère, J., Rahmani, A.: Sit to stand in elderly fallers vs non-fallers: new insights from force platform and electromyography data. Aging Clin. Exp. Res. 28(5), 871–879 (2015). https://doi.org/10.1007/s40520-015-0486-1
Arduino Nano. https://www.arduino.cc/en/uploads/Main/ArduinoNanoManual23.pdf. Accessed 27 July 2020
MyoWare Muscle Sensor v3 Datasheet. https://cdn.sparkfun.com/assets/a/3/a/f/a/AT-04-001.pdf. Accessed 27 July 2020
HX711 Load Cell Amplifier Datasheet. https://cdn.sparkfun.com/datasheets/Sensors/ForceFlex/hx711_english.pdf. Accessed 27 July 2020
MPU-6050 Datasheet. https://invensense.tdk.com/wp-content/uploads/2015/02/MPU-6000-Datasheet1.pdf. Accessed 27 July 2020
Addante, F., Gaetani, F., Patrono, L., Sancarlo, D., Sergi, I., Vergari, G.: An innovative AAL system based on IoT technologies for patients with sarcopenia. Sensors 19(22), 4951 (2019)
HC-06 Datasheet. https://www.olimex.com/Products/Components/RF/BLUETOOTH-SERIAL-HC-06/resources/hc06.pdf. Accessed 27 July 2020
Roberts, H.C., et al.: A review of the measurement of grip strength in clinical and epidemiological studies: towards a standardised approach. Age Ageing 40(4), 423–429 (2011)
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Accetta, L. et al. (2022). Integrated Measurement and Management System for Sarcopenia Diagnosis. In: Bettelli, A., Monteriù, A., Gamberini, L. (eds) Ambient Assisted Living. ForItAAL 2020. Lecture Notes in Electrical Engineering, vol 884. Springer, Cham. https://doi.org/10.1007/978-3-031-08838-4_18
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