Risk of Falling Assessment on Different Types of Ground Using the Instrumented TUG

Abstract

Abstract-Degradation of postural control observed with aging is responsible for balance problems in the elderly, especially during the activity of walking. This gradual loss of performance generates abnormal gait, and therefore increases the risk of falling. This risk worsens in people with neuronal pathologies like Parkinson and Ataxia diseases. Many clinical tests are used for fall assessment such as the Timed up and go (TUG) test. Recently, many works have improved this test by using instrumentation, especially body-worn sensors. However, during the instrumented TUG (iTUG) test, the type of ground can influence risk of falling. In this paper, we present a new methodology for fall risk assessment based on quantitative gait parameters measurement in order to improve instrumented TUG test. The proposed measurement unit is used on different types of ground, which is known to affect human gait. The methodology is closer to the real walking environment and therefore enhances ability to differentiate risks level. Our system assesses the risk of falling's level by quantitative measurement of intrinsic gait parameters using fuzzy logic. He is also able to measure environmental parameters such as temperature, humidity and atmospheric pressure for a better evaluation of the risk in activities of daily living (ADL).

Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole

“…In fact, if the current stride data is close to the computed average data, the ratio will tend to zero and the actual value (the ROFA score) will be high indicating a low risk. Based on the literature [ 70 , 74 ], this TUG score can be interpreted in three, four, or five levels. For this study, we suggested the interpretation as follows: 0 to 24 indicates a very high fall risk; 25 to 49 indicates a high fall risk; 50 to 74 indicates a medium fall risk; 75 to 99 indicates a low fall risk; 100 indicates a very low fall risk.…”

Home-Based Risk of Falling Assessment Test Using a Closed-Loop Balance Model

“…All these sensors are exploited to compute a risk level associated to a risk of falling. A 16-bits architecture microcontroller is included for computing, in real time, the risk level using different algorithms such as neural network [48], fuzzy logic [49] and our closed-loop balance model presented in this paper.…”