{"id":1832,"date":"2021-10-20T13:58:32","date_gmt":"2021-10-20T11:58:32","guid":{"rendered":"https:\/\/lbm.rcbe.de\/?page_id=1832"},"modified":"2021-10-20T14:48:17","modified_gmt":"2021-10-20T12:48:17","slug":"real-time-musculoskeletal-simulation-and-evaluation-of-data-from-wearable-smart-devices-to-precent-signs-of-aging","status":"publish","type":"page","link":"https:\/\/lbm.rcbe.de\/real-time-musculoskeletal-simulation-and-evaluation-of-data-from-wearable-smart-devices-to-precent-signs-of-aging\/","title":{"rendered":"Real-time musculoskeletal simulation and evaluation of data from wearable smart-devices to prevent signs of ageing"},"content":{"rendered":"
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Age-related diseases affect the human body in many different ways [1]. Health effects can be seen in the cardiovascular and musculoskeletal system [2, 3]. There are first approaches which describe a coherence between those effects [4\u20136]. They are mainly caused by physical inactivity and unhealthy habits [7, 8].<\/p>\n

Current social events and technical progress promote autonomous and independent ways of diagnosis and especially prevention of diseases. Exit barriers and other restrictions, which can be seen during the Corona pandemic lead to physical inactivity [9, 10]. This physical inactivity also increases with age [11, 12]. Digitalization is a great approach to intervene here. Smart devices like wearables are selling incredibly fast and offer more and more features to monitor daily movements and interactions [13\u201316]. This massive amount of data includes accelerations, number of steps and – depending on the device – several other parameters concerning the cardiovascular system [17]. Most of the data is not reviewed or cannot be interpreted by the user, because there is often no context what this data means to the personal health.<\/p>\n

More complex biomechanical data instead can be evaluated under laboratory conditions. Motion capture systems, EMG devices and load cells offer detailed information about motion patterns of subjects. This kinematic and kinetic information serves as input for musculoskeletal simulations considering individual anthropometric data of subjects [18]. Estimations about joint or muscle forces and bone loads can be made. As most of those numerical simulations are not taking fatigue or remodeling of tissue into account over a long term [19], this information can be gained from field studies.<\/p>\n

In the early 2000s, Finnish scientists did some research on how high-impact exercises (e.g. running, jumping, drop jumps) affect bone mineral density (BMD) in premenopausal (age 35-40 years) women. For that reason, all accelerations the body underwent over the day were recorded by a body monitor for 12 months and evaluated afterwards. Results show a positive correlation between a minimum amount of movements with specific high accelerations and an increased BMD in examined regions which leads to prevention of osteoporosis. [20\u201324]\n

This project aims for a feedback system controlled by artificial intelligence to merge these different biomechanical aspects and get them handsome. It makes such data more approachable, comprehensible and gives rudiments for daily exercises to prevent signs of aging and improve quality of living. Therefor user data of daily motion patterns as walking, sitting and cycling as well as exercises from literature as jumps and drop jumps will be recorded under laboratory conditions. Measurements of laboratory instruments will be compared to measurements gained from smart devices (e.g. smart watch, smartphone). Based on this data different patterns of burden will be defined, validated and serve as input information for further processing and artificial intelligence. Later, integral loads and activities of daily living should be continuously assessed according to these schemes. The user will get feedback in predefined intervals or on demand in real time on how he can improve his condition by exercise or changes of posture to prevent fatigue and loss of tissue, which causes diseases like osteoporosis or sarcopenia.<\/p>\n

The scientific approach lies in the definition and validation of patterns of burden and how corresponding data can be gained from a minimized dataset. This minimized dataset mainly contains accelerations which are used to calculate complex models. Therewith, interferences about muscle forces or bone loads are drawn. Data from exercises or motion patterns following a given program and recorded by wearables can be evaluated. This program is based on literature and clinic data. The study will show how precise musculoskeletal loads can be displayed and how good highest and constant physical stress can be measured and detected by commercial wearable devices. With success of this study we can make age prevention more tangible and an improvement of quality of living irrespective of a therapist or medical.<\/p>\n

This digital platform\/application offers more possibilities. It may be adjusted for postsurgical rehabilitation programs. Sensors can be placed at specific areas of the body recording motion patterns \u00a0in detail over the day. Attending physicians are able to see and evaluate this data and discuss it with their patients.<\/p>\n

Additionally, Biomarkers\/hormones released by muscles and bones through those exercises may be analyzed by biochemists\/physicians during this study giving a more precise prediction of preventing signs of aging.<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div>

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Phases of a Drop-Jump<\/h3>\n
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References:<\/p>\n[1]\u00a0\u00a0\u00a0\u00a0 H. P. Hirschfeld, R. Kinsella, and G. Duque, \u201cOsteosarcopenia: where bone, muscle, and fat collide,\u201d Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA<\/em>, vol. 28, no. 10, pp. 2781\u20132790, 2017, doi: 10.1007\/s00198-017-4151-8.<\/p>\n[2]\u00a0\u00a0\u00a0\u00a0 I. Liguori et al., <\/em>\u201cOxidative stress, aging, and diseases,\u201d Clinical interventions in aging<\/em>, vol. 13, pp. 757\u2013772, 2018, doi: 10.2147\/CIA.S158513.<\/p>\n[3]\u00a0\u00a0\u00a0\u00a0 G. Pizzino et al., <\/em>\u201cOxidative Stress: Harms and Benefits for Human Health,\u201d Oxidative medicine and cellular longevity<\/em>, vol. 2017, p. 8416763, 2017, doi: 10.1155\/2017\/8416763.<\/p>\n[4]\u00a0\u00a0\u00a0\u00a0 L.-K. Chen, \u201cCrosstalk Between Bone and Muscle for Healthy Aging,\u201d Aging Med Healthc<\/em>, vol. 10, no. 2, pp. 51\u201352, 2019, doi: 10.33879\/AMH.2019.1913.<\/p>\n[5]\u00a0\u00a0\u00a0\u00a0 G. Karsenty and E. N. Olson, \u201cBone and Muscle Endocrine Functions: Unexpected Paradigms of Inter-organ Communication,\u201d Cell<\/em>, vol. 164, no. 6, pp. 1248\u20131256, 2016, doi: 10.1016\/j.cell.2016.02.043.<\/p>\n[6]\u00a0\u00a0\u00a0\u00a0 M. Herrmann et al., <\/em>\u201cInteractions between Muscle and Bone-Where Physics Meets Biology,\u201d Biomolecules<\/em>, vol. 10, no. 3, 2020, doi: 10.3390\/biom10030432.<\/p>\n[7]\u00a0\u00a0\u00a0\u00a0 R. Stephen, K. Hongisto, A. Solomon, and E. L\u00f6nnroos, \u201cPhysical Activity and Alzheimer’s Disease: A Systematic Review,\u201d The journals of gerontology. Series A, Biological sciences and medical sciences<\/em>, vol. 72, no. 6, pp. 733\u2013739, 2017, doi: 10.1093\/gerona\/glw251.<\/p>\n[8]\u00a0\u00a0\u00a0\u00a0 J. Paintin, C. Cooper, and E. Dennison, \u201cOsteosarcopenia,\u201d British journal of hospital medicine (London, England : 2005)<\/em>, vol. 79, no. 5, pp. 253\u2013258, 2018, doi: 10.12968\/hmed.2018.79.5.253.<\/p>\n[9]\u00a0\u00a0\u00a0\u00a0 J. A. Woods et al., <\/em>\u201cThe COVID-19 pandemic and physical activity,\u201d Sports Medicine and Health Science<\/em>, vol. 2, no. 2, pp. 55\u201364, 2020, doi: 10.1016\/j.smhs.2020.05.006.<\/p>\n[10]\u00a0\u00a0 B. E. Ainsworth and F. Li, \u201cPhysical activity during the coronavirus disease-2019 global pandemic,\u201d Journal of sport and health science<\/em>, vol. 9, no. 4, pp. 291\u2013292, 2020, doi: 10.1016\/j.jshs.2020.06.004.<\/p>\n[11]\u00a0\u00a0 M. Gomes et al., <\/em>\u201cPhysical inactivity among older adults across Europe based on the SHARE database,\u201d Age and ageing<\/em>, vol. 46, no. 1, pp. 71\u201377, 2017, doi: 10.1093\/ageing\/afw165.<\/p>\n[12]\u00a0\u00a0 L. Bonewald, \u201cUse it or lose it to age: A review of bone and muscle communication,\u201d Bone<\/em>, vol. 120, pp. 212\u2013218, 2019, doi: 10.1016\/j.bone.2018.11.002.<\/p>\n[13]\u00a0\u00a0 IDC, Impact of coronavirus (COVID-19) outbreak on global wearables market growth forecast in 2019, 2020 and 2024. <\/em>[Online]. Available: https:\/\/\u200bwww.statista.com\u200b\/\u200bstatistics\/\u200b1106297\/\u200bworldwide-\u200bwearables-\u200bmarket-\u200bgrowth-\u200bimpacted-\u200bby-\u200bcovid-\u200b19-\u200boutbreak\/\u200b (accessed: Dec. 21 2020, 1 PM).<\/p>\n[14]\u00a0\u00a0 D. de Rossi and P. Veltink, \u201cWearable technology for biomechanics: e-textile or micromechanical sensors?,\u201d IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society<\/em>, vol. 29, no. 3, pp. 37\u201343, 2010, doi: 10.1109\/MEMB.2010.936555.<\/p>\n[15]\u00a0\u00a0 TrendForce, Smartwatch unit shipments worldwide from 2016 to 2022 (in millions): Unit shipment of smartwatches worldwide 2016-2022. <\/em>[Online]. Available: https:\/\/\u200bwww.statista.com\u200b\/\u200bstudy\/\u200b15607\/\u200bwearables-\u200bstatista-\u200bdossier\/\u200b (accessed: Dec. 21 2020PM).<\/p>\n[16]\u00a0\u00a0 Statista Global Consumer Survey, Do you personally use wearables (e.g. smart watch, health \/ fitness tracker)? <\/em>[Online]. Available: https:\/\/\u200bwww.statista.com\u200b\/\u200bforecasts\/\u200b1101110\/\u200bwearables-\u200bdevices-\u200busage-\u200bin-\u200bselected-\u200bcountries (accessed: Dec. 21 2020PM).<\/p>\n[17]\u00a0\u00a0 C. Dinh-Le, R. Chuang, S. Chokshi, and D. Mann, \u201cWearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions,\u201d JMIR mHealth and uHealth<\/em>, vol. 7, no. 9, e12861, 2019, doi: 10.2196\/12861.<\/p>\n

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Age-related diseases affect the human body in many different ways [1]. Health effects can be seen in the cardiovascular and musculoskeletal system [2, 3]. There are first approaches which describe a coherence between those effects [4\u20136]. They are mainly caused by physical inactivity and unhealthy habits [7, 8]. Current social events and technical progress promote…<\/p>\n","protected":false},"author":30,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1832","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/lbm.rcbe.de\/wp-json\/wp\/v2\/pages\/1832"}],"collection":[{"href":"https:\/\/lbm.rcbe.de\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/lbm.rcbe.de\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/lbm.rcbe.de\/wp-json\/wp\/v2\/users\/30"}],"replies":[{"embeddable":true,"href":"https:\/\/lbm.rcbe.de\/wp-json\/wp\/v2\/comments?post=1832"}],"version-history":[{"count":7,"href":"https:\/\/lbm.rcbe.de\/wp-json\/wp\/v2\/pages\/1832\/revisions"}],"predecessor-version":[{"id":1843,"href":"https:\/\/lbm.rcbe.de\/wp-json\/wp\/v2\/pages\/1832\/revisions\/1843"}],"wp:attachment":[{"href":"https:\/\/lbm.rcbe.de\/wp-json\/wp\/v2\/media?parent=1832"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}