Difference between revisions of "HMC2"
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For activities such as cycling, muscle fatigue is a major concern as high levels of muscle fatigue can cause serious injuries. It occurs locally and is characterized by the declining ability to perform muscle contractions and force exertions. A person experiencing muscle fatigue will generally feel pain in those muscles. Objective assessment of muscle fatigue would help athletes, trainers and hobby cyclists to assess fitness and prevent muscle injuries. Analysis of electromyographic (EMG) signals can be applied to evaluate local muscle fatigue. Stationary equipment is usually used to record EMG signals. | For activities such as cycling, muscle fatigue is a major concern as high levels of muscle fatigue can cause serious injuries. It occurs locally and is characterized by the declining ability to perform muscle contractions and force exertions. A person experiencing muscle fatigue will generally feel pain in those muscles. Objective assessment of muscle fatigue would help athletes, trainers and hobby cyclists to assess fitness and prevent muscle injuries. Analysis of electromyographic (EMG) signals can be applied to evaluate local muscle fatigue. Stationary equipment is usually used to record EMG signals. | ||
| + | |||
| + | == Approach == | ||
| + | |||
| + | *Inertial Sensors: The approach is to use wearable sensors, typically accelerometers, to estimate motion intensities and qualities and estimate energy consumption. The person wearing the sensors can get immediate (or almost immediate) feedback as well as a time log of energy consumption. | ||
| + | |||
| + | *EMG: The approach is to use EMG sensors weaved into a wearable textile belt, which can be comfortably put on a leg. Other modalities than EMG sensors can also be weaved. Some processing can be done in the sensors and for e.g., a smart phone can be used for more advanced analysis. The person wearing the belt can therefore get immediate information on muscle status as well as results of a comprehensive analysis of the data after the training session. | ||
| + | |||
| + | == Results so far == | ||
| + | |||
| + | *Inertial Sensors: | ||
Revision as of 09:33, 19 May 2014
Human Motion, Categorization and Characterization
| HMC2 | |
| Project start: | |
|---|---|
| 1 January 2012 | |
| Project end: | |
| 31 December 2015 | |
| More info (PDF): | |
| Contact: | |
| Nicholas Wickström | |
| Application Area: | |
| Health Technology | |
Involved internal personnel
| |
| |
Involved external personnel
| |
Involved partners
| |
Abstract
The objective is to design wearable instruments/devices that can characterize and classify human motion. The devices shall be small and “non-intrusive”, similar to a step counter. We are looking at two sensor modalities: inertial sensors (e.g. accelerometers and gyros) and electrical sensors for detecting, e.g., electromyography (EMG) signals.
Background and Motivation
Moderate physical activity can improve health substantially over an individual’s lifetime. Even physically active people can improve their health status by increasing their activity. Physical activity helps to control diseases; adopting healthy behaviors help in decreasing and controlling the effects of diseases like, e.g., diabetes, heart disease, depressions and dementia. There are two important aspects in the problem of increasing the level of physical activity for an individual:
- One is the matter of having a portable method/device for measuring an individual’s level of physical activity.
- The other is the ability to motivate the individual by providing suitable feedback on his/her level of activity.
It is important to develop simple and wearable devices that can measure the daily physical activity of an individual. Such devices can either be used both to monitor physical activity, e.g. in a treatment scenario or to gauge the levels of physical activity in a cohort, or to motivate physical activity. A simple example of such a device is the step counter, though it provides a very coarse measurement and only measures the activity during walking or running. Thus, if a portable device (preferably wearable) would be available that could “ubiquitously” measure different sorts of physical activity and estimate the intensity level, then it would open up new frontiers promoting treatments and pro-active approaches to better health.
For activities such as cycling, muscle fatigue is a major concern as high levels of muscle fatigue can cause serious injuries. It occurs locally and is characterized by the declining ability to perform muscle contractions and force exertions. A person experiencing muscle fatigue will generally feel pain in those muscles. Objective assessment of muscle fatigue would help athletes, trainers and hobby cyclists to assess fitness and prevent muscle injuries. Analysis of electromyographic (EMG) signals can be applied to evaluate local muscle fatigue. Stationary equipment is usually used to record EMG signals.
Approach
- Inertial Sensors: The approach is to use wearable sensors, typically accelerometers, to estimate motion intensities and qualities and estimate energy consumption. The person wearing the sensors can get immediate (or almost immediate) feedback as well as a time log of energy consumption.
- EMG: The approach is to use EMG sensors weaved into a wearable textile belt, which can be comfortably put on a leg. Other modalities than EMG sensors can also be weaved. Some processing can be done in the sensors and for e.g., a smart phone can be used for more advanced analysis. The person wearing the belt can therefore get immediate information on muscle status as well as results of a comprehensive analysis of the data after the training session.
Results so far
- Inertial Sensors:
