|Title:||Activity monitoring in daily life using Shimmer sensing|
Wearable devices with advanced sensors become more and more used not only for fitness but also for identifying potential health issues. In an aging population fall detection and fall risk assessment is crucial. In Sweden the cost for society is 26.4 billion every year. Sensors can play an important role in early warning system to assess the risk of falling.
A wearable device collects data from the senior citizen and an expert system in the cloud will analyze the time series to detect deviations from a normal behavior. This is especially useful for elderly persons living alone in their home environment.
A first step was made in the “fallen angel” project spring 2016 focusing on fall detection. Now it is time to look at predicting increased risk of falling.
The task is to identify activities in daily life (ADL), communicate it to a cloud server and provide trends and changes in user behavior over time.
The ADL recognition and analysis will be done in an Android app but also communicated to an existing cloud service.
The ADL is checked regular every 5-15 minute and the activity is tested against a pre-defined set of ADL common to most elderly persons.
The task includes a theoretical analysis of AI algorithm to be used, collection of training data to recognize activities and a model for identify “normal activity” and “deviations from normal activity” for an individual.
The project will use any suitable wearable device such as Shimmer sensing.
- ADL recognition using machine learning algorithms
- A working android application monitoring ADL and changes in user behavior
- Database with training sets for at least 10 ADL
Lessons learned from the implementation
|Prerequisites:||Programming Skills and knowledge on Machine learning algorithms|
|IDT supervisors:||Mobyen Uddin Ahmed|
|Company contact:||lifescience technology Peter Eriksson Peter.Eriksson@lifescience-technology.com|