Abstract
Tremor is most common among the movement disabilities that affect older people, having a prevalence rate of 4.6% in the population older than 65 years. Despite this, distinguishing different types of tremors is clinically challenging, often leading to misdiagnosis. However, due to advances in microelectronics and wireless communication, it is now possible to easily monitor tremor in hospitals and even in home environments. In this paper, we propose an architecture of a system for remote health-care and one possible implementation of such system focused on head tremor monitoring. In particular, the aim of the study presented here was to test new tools for differentiating essential tremor from dystonic tremor. To that aim, we propose a number of temporal and spectral features that are calculated from measured gyroscope signals, and identify those that provide optimal differentiation between two groups. The mean signal amplitude feature results in sensitivity = 0.8537 and specificity = 0.8039 in distinguishing patients having cervical dystonia with or without tremor. In addition, mean signal amplitude was shown to be significantly higher in patients with essential tremor than in patients with cervical dystonia, whereas the mean peak frequency is not different between two groups.
Highlights
In recent years, most of the countries in the world have experienced the rise in life expectancy and the increase in aging population
Principal component analysis (PCA) is performed on the 3D signal obtained from the gyroscope (The reason why, in this work, we considered the gyroscope measurement is because the head tremor is rotational motion, which can be properly sensed by a gyroscope, free of gravitational effect, as it is shown in [21].) sensor in order to isolate the dominant axis of the tremor, to [18]
We proposed an architecture for remote health care within broader Ambient Assisted
Summary
Most of the countries in the world have experienced the rise in life expectancy and the increase in aging population. This trend, which initially emerged in developed countries, can be observed in almost all developing countries. It is estimated that, by the year 2050, 16% of the world’s population will be over age 65, in comparison to 9% in 2019. These trends have significantly contributed to increased incidence rate of disabilities and chronic diseases which put additional demands on the health care systems [2]. One of the possible solutions to deal with this challenge, which could enable elderly and disabled people to live independently for as long as possible in their own homes, is to apply Ambient Assisted Living (AAL) concepts within long-term health care support
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