Researchers at Chalmers University of Technology in Sweden have developed a method for predicting fall accidents and cognitive illnesses such as Alzheimer’s disease by reading a person’s walking pattern with the aid of a radar sensor.
The small sensor can be attached to furniture, walls and ceilings, both in the home and in a healthcare setting. The new tool was demonstrated in the latest issue of the journal Sensors.
“Our method is both precise and easy to use. It can help healthcare staff to carry out a more reliable risk analysis and tailor interventions to achieve a significant effect early on,” says Xuezhi Zeng, a researcher in biomedical electromagnetics at Chalmers University of Technology.
Fall accidents and cognitive illnesses such as Alzheimer’s disease are increasing as the population ages. Preventive measures are helpful and can reduce both suffering and costs. The new method devised by the Chalmers’ researchers uses a small radar sensor to acquire real-time, high-resolution reading of a person’s walking pattern, especially the time required to take a step, which helps predict risks.
“It is the variation in step times that is the key. A healthy person normally has a regular gait. But a person at risk of fall accidents often has a large variation in step times. For example, the first step may take a second whereas the second may take two seconds,” says Zeng.
A product containing the sensor is no larger than a fire alarm and could be used within the healthcare system, in the home or in care environments for the elderly in order to identify risks. Preventive measures such as physiotherapy, tailored training or the adaptation of furnishing in the home can be implemented in order to prevent fall accidents, thus avoiding both suffering and costly hospital care.
Apart from being easy to use, another advantage of the method is that it collects data without filming. This means that it can be used without invading people’s privacy and integrity, and without the feeling of monitoring that something such as a camera would give.
“Also, with cognitive illnesses such as Alzheimer’s, an increase in step time variability is often an early symptom. Alzheimer’s disease is one of the most common causes of dementia in the world, and it is difficult to detect at an early stage. Here too, the method could be beneficial as an aid to making an early diagnosis, and contribute to preventive measures and an improved quality of life,” Zeng concludes.
