Characterizing Dynamic Walking Patterns and Detecting Falls with Wearable Sensors Using Gaussian Process Methods

Abstract

By incorporating a growing number of sensors and adopting machine learning technologies, wearable devices have recently become a prominent health care application domain. Among the related research topics in this field, one of the most important issues is detecting falls while walking. Since such falls may lead to serious injuries, automatically and promptly detecting them during daily use of smartphones and/or smart watches is a particular need. In this paper, we investigate the use of Gaussian process (GP) methods for characterizing dynamic walking patterns and detecting falls while walking with built-in wearable sensors in smartphones and/or smartwatches. For the task of characterizing dynamic walking patterns in a low-dimensional latent feature space, we propose a novel approach called auto-encoded Gaussian process dynamical model, in which we combine a GP-based state space modeling method with a nonlinear dimensionality reduction method in a unique manner. The Gaussian process methods are fit for this task because one of the most import strengths of the Gaussian process methods is its capability of handling uncertainty in the model parameters. Also for detecting falls while walking, we propose to recycle the latent samples generated in training the auto-encoded Gaussian process dynamical model for GP-based novelty detection, which can lead to an efficient and seamless solution to the detection task. Experimental results show that the combined use of these GP-based methods can yield promising results for characterizing dynamic walking patterns and detecting falls while walking with the wearable sensors.

Gauss process state-space model optimization algorithm with expectation maximization

“…In simulation experiments, the BFGS algorithm 21 is used to train hyperparameters of the GPSSM*, which are recorded in Table 2. (X Ã 0:50 , Y Ã 1:50 ) are the input data and the output data to construct the kernel functions of the GPSSMs [1][2][3][4] . fY i 1:50 g 4 i = 1 are taken as measurements in these non-laboratory environments.…”

“…fY i 1:50 g 4 i = 1 are taken as measurements in these non-laboratory environments. The EM-GP-RTSS is adopted to optimize the GPSSM* in order to obtain the GPSSMs [1][2][3][4] whose hyperparameters are recorded in Table 3. The kernel functions k f 1 ,k f 2 , and k h in Table 3 are defined in formulas (15) and (16).…”

“…and fs h , W h , s y g where W f i = ½s 2 f i, 1 , 0; 0, s 2 f i, 2 and W h = ½s 2 h 1 , 0; 0, s 2 h 2 . fX i 0:50 , Y i 1:50 g 4 i = 1 are obtained by running the pendulum models [1][2][3][4] separately, where X i 0:50 is unknown in the non-laboratory environment. If the GPSSM* is used to estimate X i 0:50 given Y i 1:50 , there is an overfitting issue.…”

“…For example, GPSSMs are used for target monitoring and location problems in wireless sensor networks. 1,2 In order to train the GPSSM of a nonlinear system, its true system states are required. However, paradoxically, its states are usually unknown and needed to be estimated.…”

Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

“…The motivation for environmental health is ensuring the wellness of communities in a smart city for sustainability. The body area network within a smart city can be used for ECG monitoring with the aim of warning an individual of any heart-related problem, especially cardiac arrest [13], and also helps in determining the nature of human kinematic actions with the aim of ensuring improved quality of healthcare whenever needed [14]. Data management of patients is also one of the applications of smart health in smart cities that is of paramount importance.…”