Walking Recognition in Mobile Devices

Abstract

Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localization, and navigation. The information captured by the inertial sensors of the phone (accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performed by the person who is carrying the device, in particular in the activity of walking. Nevertheless, the development of a standalone application able to detect the walking activity starting only from the data provided by these inertial sensors is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the smartphone can experience and which have nothing to do with the physical displacement of the owner. In this work, we explore and compare several approaches for identifying the walking activity. We categorize them into two main groups: the first one uses features extracted from the inertial data, whereas the second one analyzes the characteristic shape of the time series made up of the sensors readings. Due to the lack of public datasets of inertial data from smartphones for the recognition of human activity under no constraints, we collected data from 77 different people who were not connected to this research. Using this dataset, which we published online, we performed an extensive experimental validation and comparison of our proposals.

Concept drift detection and adaptation for federated and continual learning

“…For the model, we used a Convolutional Neural Network (CNN), given the performance this type of network has shown for inertial signal processing [8,28]. However, there is nothing preventing CDA-FedAvg algorithm from being applied using a different type of network, like any other feed-forward architecture; recurrent neural networks (RNNs) such as long short-term memory (LSTM); or even a hybrid approach.…”

A systematic review of smartphone-based human activity recognition methods for health research

“…Less common activities involved various types of mobility, locomotion, fitness, and household routines, e.g., slow, normal, and brisk walking 24 , multiple transportation modes, such as by car, bus, tram, train, metro, and ferry 25 , sharp body-turns 26 , and household activities, like sweeping a floor or walking with a shopping bag 27 . More recent studies concentrated solely on walking recognition 28,29 . As shown in Fig.…”

“…However, we also noted several approaches that automated this process both in controlled and free-living conditions, e.g., through a designated smartphone application 22 or built-in step counter combined paired with GPS data 53 ., used a built-in step counter and GPS data to produce "weak" labels. The annotation was also done using the built-in microphone 54 , video camera 18,20 , or an additional body-worn sensor 29 .…”

Human and Animal Motion Tracking Using Inertial Sensors

“…The second research paper with innovative methods explores the feasibility of use of inertial sensors of smartphones for walking recognition [11]. In their article, the authors suggest analyzing several metrics of the motion deduced from the inertial sensors of the smartphone using various machine learning techniques to identify the gait of the subject.…”