Phone Based Fall Risk Prediction

Falls are a major health risk that diminishes the quality of life among older people and increases health services cost. With the increasing number of older people in the population, management of falls is becoming a challenge. Reliable and earlier prediction of an increased fall risk is therefore essential to improve its prevention, aiming to avoid the occurrence of falls.

In this project, mobile phone is the main platform for developing a multidimensional fall prediction system by running an inertial sensor based fall prediction algorithm. In this approach, the phone is attached at the lower back of trunk, and a recording of sensor signals is done during walking, which enables the extraction of several gait parameters having a relation with risk. Other risk factors for falling can also be assessed, using some questionnaires.

Risks can therefore be identified and monitored over time. Since the information is stored in the smartphone, the historical can be used by the user and/or automatically transmitted to the doctor by gateway capabilities in order to evaluate the falling risk over time and apply earlier preventive strategies or modify the ineffective interventions.

Experimental results of the system, which are still work in progress, are encouraging making us optimistic regarding the feasibility of a reliable phone-based fall predictor, which can be of great value for older persons and society.



Presentation - Phone Based Fall Risk Prediction

MSc Thesis - Phone Based Fall Risk Prediction


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