Real-time musculoskeletal simulation and evaluation of data from wearable smart-devices to prevent signs of ageing

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 [4–6]. They are mainly caused by physical inactivity and unhealthy habits [7, 8].

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 [13–16]. 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.

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.

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. [20–24]

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.

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.

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  in detail over the day. Attending physicians are able to see and evaluate this data and discuss it with their patients.

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.

Phases of a Drop-Jump


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