Delete search term

To content

Main navigation

Zurich University
of Applied Sciences

Service navigation

Data Driven Medical muscle Training

Description

Muscle loss is a concomitant of various diseases (cachexia) and the usual aging process (sarcopenia). Limiting this reduction and, ideally, even reversing it makes medical and economic sense. In medicine, the metabolic function (in addition to the pure movement) of the muscles is increasingly understood and recognized in their therapeutic relevance; for chronic diseases, e.g. HIV or cancer has been shown to help patients with higher muscle mass survive longer. From the point of view of most older people as well as from an economic perspective, it is desirable to make it possible to stay as long as possible in one's own living space. Well-developed muscles are a necessary prerequisite for this.Strength training is an established measure against cachexia / sarcopenia. At present, such training is characterized by a few non-dynamic parameters (type of exercise, number of repetitions, force to be applied). Recent findings (from molecular physiology and exercise science) suggest that the success of strength training is influenced not only by these static parameters, but also by the way in which movements are performed. The Institute of Systems Biology (Prof. Dr. Ernst Hafen) and the Laboratory of Sports Physiology (Prof. Dr. Christina Spengler) of the ETH Zurich are investigating ways to better parameterize training through additional mechano-biological descriptors, to characterize their relevance for muscle building and as a consequence to make the trainings accessible to further personalization and optimization. Specifically, not only the number of repetitions of a movement, but also the details of the execution (in particular the acceleration profiles) should be analyzed. The ultimate goal is to make patients optimally manage the movement during a workout with respect to stimulating muscle building.

Key data

Deputy Projectlead

Project partners

Eidgenössische Technische Hochschule Zürich ETHZ / Exercise Physiology Lab

Project status

completed, 02/2019 - 11/2019

Institute/Centre

School of Engineering; Institute of Embedded Systems (InES); Institute of Computer Science (InIT); Institute of Applied Mathematics and Physics (IAMP)

Funding partner

Internal

Privacy Settings

Here we show you which services may run on our website. You decide for yourself which services we may use. You can find more information in our privacy notice.

Our website needs these cookies so that everything works properly. These cookies are created automatically when you are active on the site – for example, when you change privacy settings or fill in forms. You can block these cookies in your browser or set it to warn you about them, but some parts of the website will then no longer display correctly. These cookies do not store any of your personal data.

These services allow us to see how our website is used, but we do not know who you are. By activating these services, you help us make the website better for everyone.