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

Ivan Pires, Nuno Garcia, Nuno Pombo, Francisco Flórez-Revuelta
2016 Sensors  
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
more » ... essing 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). The identification of ADLs using sensors available in off-the-shelf mobile devices is one of the most interesting goals for AAL solutions, as this can be used for the monitoring and learning of a user's lifestyle. Focusing on off-the-shelf mobile devices, these solutions may improve the user's quality of life and health, achieving behavioural changes, such as to reduce smoking or control other addictive habits. This paper does not comprehend the identification of ADLs in personal health or well-being, as this application ecosystem is far wider and deserves a more focused research, therefore being addressed in future work. AAL has been an important area for research and development due to population ageing and to the need to solve societal and economic problems that arise with an ageing society. Among other areas, AAL systems employ technologies for supporting personal health and social care solutions. These systems mainly focus on elderly people and persons with some type of disability to improve their quality of life and manage the degree of independent living [1,2]. The pervasive use of mobile devices that incorporate different sensors, allowing the acquisition of data related to physiological processes, makes these devices a common choice as AAL systems, not only because the mobile devices can combine data captured with their sensors with personal information, such as, e.g., the user's texting habits or browsing history, but also with other information, such as the user's location and environment. These data may be processed either in the device or sent to a server using communication technologies for later processing [3], requiring a high level of quality of service (QoS) to be needed to achieve interoperability, usability, accuracy and security [2] . The concept of AAL also includes the use of sophisticated intelligent sensor networks combined with ubiquitous computing applications with new concepts, products and services for P4-medicine (preventive, participatory, predictive and personalized). Holzinger et al. [4] present a new approach using big data to work from smart health towards the smart hospital concept, with the goal of providing support to health assistants to facilitate a healthier life, wellness and wellbeing for the overall population. Sensors are classified into several categories, taking into account different criteria, which include the environmental analysis and the type of data acquired. The number and type of sensors available in an off-the-shelf mobile device are limited due to a number of factors, which include the reduced processing capacity and battery life, size and form design and placement of the mobile device during the data acquisition. The number and type of available sensors depend on the selected mobile platform, with variants imposed by the manufacturer, operating system and model. Furthermore, the number and type of sensors available are different between Android platforms [5] and iOS platforms [6] . Off-the-shelf mobile devices may include an accelerometer, a magnetometer, an ambient air temperature sensor, a pressure sensor, a light sensor (e.g., photometer), a humidity sensor, an air pressure sensor (e.g., hygrometer and barometer), a Global Positioning System (GPS) receiver, a gravity sensor, a gyroscope, a fingerprint sensor, a rotational sensor, an orientation sensor, a microphone, a digital camera and a proximity sensor. These sensors may be organized into different categories, which we present in Section 2, defining a new classification of these sensors. Jointly with this classification, the suitability of the use of these sensors in mobile systems for the recognition of ADLs is also evaluated.
doi:10.3390/s16020184 pmid:26848664 pmcid:PMC4801561 fatcat:ushwthv6xrcgbi7lc2sejwgtje