Ivan Miguel Pires

From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

This paper presents a PhD project related to the identification of a set of Activities of Daily Living (ADLs) using different techniques applied to the sensors available in off-the-shelf mobile devices. This project consists on the creation of new methodologies, to identify ADLs, and to present some concepts, such as definition of the set of ADLs relevant to be identified, the mobile device as a multi-sensor system, review of the best techniques for data acquisition, data processing, data validation, data imputation, and data fusion processes, and creation of the methods for the identification of ADLs with data mining, pattern recognition and/or machine learning techniques. However, mobile devices present several limitations, therefore techniques at each stage have to be adapted. As result of this study, we presented a brief review of the state-of-the-art related to the several parts of a mobile-system for the identification of the ADLs. Currently, the main focus consists on the study for the creation of a new method based on the analysis of audio fingerprinting samples in some Ambient Assisted Living (AAL) scenarios.

Validation Techniques for Sensor Data in Mobile Health Applications

Mobile applications have become a must in every user’s smart device, and many of these applications make use of the device sensors’ to achieve its goal. Nevertheless, it remains fairly unknown to the user to which extent the data the applications use can be relied upon and, therefore, to which extent the output of a given application is trustworthy or not. To help developers and researchers and to provide a common ground of data validation algorithms and techniques, this paper presents a review of the most commonly used data validation algorithms, along with its usage scenarios, and proposes a classification for these algorithms. This paper also discusses the process of achieving statistical significance and trust for the desired output.

mHealth Sensors and Applications for Personal Aid

The evolution of medical equipment and health care involves the miniaturization and autonomy of devices that are responsible for medical monitoring, screening and even therapeutic actions.

Conceptual Definition of a Platform for the Monitoring of the Subjects with Nephrolithiasis Based on the Energy Expenditure and the Activities of Daily Living Performed

Nephrolithiasis disease is commonly related with the low activity performance, i.e., the regular performance of physical activity can reduce the risk of kidney stones. Sensors available in off-the-shelf mobile devices may handle the control and recognition of the activities performed, including the energy expenditure and their identification. This paper identifies the common values that should be measured during the treatment of this disease, including water consumption (with regular registration), daily calories intake (defined by a professional) and urinary pH (measured with test strips), which may be combined with the measurement of the energy expenditure and the activities performed. As the treatment and prevention of the Nephrolithiasis disease includes the performance of hard physical activity and the regular trip to the toilet, where this identification provides a control of the evolution of the treatment. The combination of these concepts and the use of the technology may increase the control and speed of the treatment.

Multi-Sensor Mobile Platform for the Recognition of Activities of Daily Living and their Environments based on Artificial Neural Networks.

The recognition of Activities of Daily Living (ADL) and their environments based on sensors available in off-the-shelf mobile devices is an emerging topic. These devices are capable to acquire and process the sensors’ data for the correct recognition of the ADL and their environments, providing a fast and reliable feedback to the user. However, the methods implemented in a mobile application for this purpose should be adapted to the low resources of these devices. This paper focuses on the demonstration of a mobile application that implements a framework, that forks their implementation in several modules, including data acquisition, data processing, data fusion and classification methods based on the sensors’ data acquired from the accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver. The framework presented is a function of the number of sensors available in the mobile devices and implements the classification with Deep Neural Networks (DNN) that reports an accuracy between 58.02% and 89.15%.

Limitations of the Use of Mobile Devices and Smart Environments for the Monitoring of Ageing People.

The monitoring of the daily life of ageing people is a research topic widely explored by several authors, which they presented different points of view. The different research studies related to this topic have been performed with mobile devices and smart environments, combining the use of several sensors and techniques in order to handle the recognition of Activities of Daily Living (ADL) that may be used to monitor the lifestyle and improve the life’s quality of the ageing people. However, the use of the mobile devices has several limitations, including the low power processing and the battery life. This paper presents some different points of view about the limitations, combining them with a research about use of a mobile application for the recognition of activities. At the end, we conclude that the use of lightweight methods with local processing in mobile devices is the best method to the recognition of the ADL of ageing people in order to present a fast feedback about their lifestyle. Finally, for the recognition of the activities in a restricted space with constant network connection, the use of smart environments is more reliable than the use of mobile devices.

Measurement of the Reaction Time in the 30-S Chair Stand Test using the Accelerometer Sensor Available in off-the-Shelf Mobile Devices

The 30-s Chair Stand Test (CST) is commonly used with elderly people for assessing the lower limbs strength, which can provide sufficient information regarding the general mobility and fall risk. The mobile devices are widely used for the acquisition of the different physical and physiological data from the sensors available, including the accelerometer. In this way, the aim of the present study consisted on the development of an automatic method for the measurement of the reaction time (RT) based on the 30-s CST using a mobile device. Besides that, the data acquisition through an accelerometer allows the assessment of different variables, such as the maximum values of the acceleration, the instant velocity, the maximum force and the peak power, that may contribute to a better understanding of the physical demands during the 30-s CST performance. The results presented in this study demonstrated that the calculation of the RT and the different variables during the 30-s CST performance is possible, opening new possibilities for the development of scientific projects, namely those that encompasses the motor and cognitive training of

Framework for the Recognition of Activities of Daily Living and Their Environments in the Development of a Personal Digital Life Coach.

Due to the commodity of the use of the off-the-shelf mobile devices and technological devices by ageing people, the automatic recognition of the Activities of Daily Living (ADL) and their environments using these devices is a research topic were studied in the last years, but this project consists in the creation of an automatic method that recognizes a defined dataset of ADL using a large set of sensors available in these devices, such as the accelerometer, the gyroscope, the magnetometer, the microphone and the Global Positioning System (GPS) receiver. The fusion of the data acquired from the selected sensors allows the recognition of an increasing number of ADL and environments, where the ADL are mainly recognized with motion, magnetic and location sensors, but the environments are mainly recognized with acoustic sensors. During this project, several methods have been researched in the literature, implementing three types of neural networks, these are Multilayer Perceptron (MLP) with Backpropagation, Feedforward neural network (FNN) with Backpropagation and Deep Neural Networks (DNN), verifying that the neural networks that report highest results are the DNN method for the recognition of ADL and standing activities, and the FNN method for the recognition of environments.

Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions

This work was supported by FCT project UID/EEA/50008/2013 (Este trabalho foi suportado pelo projecto FCT UID/EEA/50008/2013). The authors would also like to acknowledge the contribution of the COST Action IC1303 – AAPELE – Architectures, Algorithms and Protocols for Enhanced Living Environments.