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Abstract—People at the age of 65 and above are drastically at the risk of falling. Falls among el... more Abstract—People at the age of 65 and above are drastically at the risk of falling. Falls among elderly may occur because of many physical factors such as natural deterioration of motor function or other disease related problems such as stroke, Parkinson’s or Alzheimer’s disease. The methods reported in the literature to assess the risk of falling in older people are often subjective, qualitative, retrospective and inaccurate. An objective, quantifiable approach to assess the risk of falling in elderly by analysing body movement using inertial sensors is conducted. Initial results of the experimental work and analysis carried out on three subjects, one older person with impaired balance and two younger people with normal balance are reported. The sensors are mounted on the subjects’ chests and they are asked to conduct balance tests. The results of the data analysis indicate that the elderly subject has disturbed and involuntary chest movements. This demonstrates the potential of the method to assess balance and the risk of fall in older people.
Almost 30% of people above 65 years of age fall each year all over the world. This figure increas... more Almost 30% of people above 65 years of age fall each year all over the world. This figure increases to 40% for people
older than 80. Falls have the highest percentage of injury‐related deaths and fall related injuries have the highest cost
after vehicle injuries. The mortality rate of fall increases dramatically with age and 70% of accidental deaths in persons
above 75 are caused by fall. Fall in older people have many severe outcomes. Fall is the cause of 10% of elderly
rushing to the hospitals and 6% of further hospitalization. The fall consequences can be physical such as fractures, as
well as social such as isolation and depression. It is proved that frail elderly fallers display a significantly slower walking
speed than non‐fallers. Using inertial motion data, a stochastic method, based on Gaussian Mixture Model is used to identify
deviation from normal gait. The results obtained so far indicate that the proposed method can assess the risk of fall
caused by aging.
Abstract—People at the age of 65 and above are drastically at the risk of falling. Falls among el... more Abstract—People at the age of 65 and above are drastically at the risk of falling. Falls among elderly may occur because of many physical factors such as natural deterioration of motor function or other disease related problems such as stroke, Parkinson’s or Alzheimer’s disease. The methods reported in the literature to assess the risk of falling in older people are often subjective, qualitative, retrospective and inaccurate. An objective, quantifiable approach to assess the risk of falling in elderly by analysing body movement using inertial sensors is conducted. Initial results of the experimental work and analysis carried out on three subjects, one older person with impaired balance and two younger people with normal balance are reported. The sensors are mounted on the subjects’ chests and they are asked to conduct balance tests. The results of the data analysis indicate that the elderly subject has disturbed and involuntary chest movements. This demonstrates the potential of the method to assess balance and the risk of fall in older people.
Almost 30% of people above 65 years of age fall each year all over the world. This figure increas... more Almost 30% of people above 65 years of age fall each year all over the world. This figure increases to 40% for people
older than 80. Falls have the highest percentage of injury‐related deaths and fall related injuries have the highest cost
after vehicle injuries. The mortality rate of fall increases dramatically with age and 70% of accidental deaths in persons
above 75 are caused by fall. Fall in older people have many severe outcomes. Fall is the cause of 10% of elderly
rushing to the hospitals and 6% of further hospitalization. The fall consequences can be physical such as fractures, as
well as social such as isolation and depression. It is proved that frail elderly fallers display a significantly slower walking
speed than non‐fallers. Using inertial motion data, a stochastic method, based on Gaussian Mixture Model is used to identify
deviation from normal gait. The results obtained so far indicate that the proposed method can assess the risk of fall
caused by aging.