Smart Assistance to Reduce the Fear of Falling in Parkinson Patients Using IoT

Parkinson's disease (PD) is often classified as a neuro-degenerative movement disorder that is related with the gait and balance difficulties and a significantly increased danger of falling. Multipart movements, like the turns, may often cause instability in the balance and result in a fall. Falls in PD may necessitate an increased assistance and lead to the higher possibility of hospitalization together with a lethal effect on the lifestyle quality. So, there is a high demand for a fall detection system (FDS) for the PD patients which may assist them to reduce the fear of falling (FoF) and will also improve the patient care services. The threshold based fall detection systems has faster response time and lesser resource requirements compared to the machine learning based design. In this paper, we proposed a smart threshold based FDS with a nominal computational overhead, using an on board tri-axial accelerometer of the smartphone. The proposed FDS improves the overall system accuracy at the time of post fall emergency situation compared to the other traditional fall supervision techniques. The system has 94.45% accuracy and could reduce the FoF of the PD patients upto 10% in some cases.