An evaluation of the effectiveness of personalization and self-adaptation for e-Health apps

Context: There are many e-Health mobile apps on the apps store, from apps to improve a user\u27s lifestyle to mental coaching. Whilst these apps might consider user context when they give their interventions, prompts, and encouragements, they still tend to be rigid e.g., not using user context and experience to tailor themselves to the user. Objective: To better engage and tailor to the user, we have previously proposed a Reference Architecture for enabling self-adaptation and AI personalization in e-Health mobile apps. In this work we evaluate the end users’ perception, usability, performance impact, and energy consumption contributed by this Reference Architecture. Method: We do so by implementing a Reference Architecture compliant app and conducting two experiments: a user study and a measurement-based experiment. Results: Although limited in the number of participants, the results of our user study show that usability of the Reference Architecture compliant app is similar to the control app. Users’ perception was found to be positively influenced by the compliant app when compared to the control group. Results of our measurement-based experiment showed some differences in performance and energy consumption measurements between the two apps. The differences are, however, deemed minimal. Conclusions: Our experiments show promising results for an app implemented following our proposed Reference Architecture. This is preliminary evidence that the use of personalization and self-adaptation techniques can be beneficial within the domain of e-Health apps

Personalized Nudges with Edge Computing

This thesis aims to investigate the role of edge computing in a smart nudging system. A smart nudging system has requirements for efficient data processing of personal and context-aware data from heterogeneous sources. Furthermore, a smart nudging system needs to protect and preserve the privacy of data within the system. Edge computing has been proposed as a computing paradigm in a smart nudging system to accommodate some of these requirements. The edge computing paradigm makes promises of low latency, context-aware data collection and contributions to privacy when running on an edge device. However, edge computing has limitations in resources for heavy computations and storage. Therefore, a smart nudging system, NuEdge, has been proposed to utilize edge computing resources integrated with cloud computing, a local server, and IoT devices for better performance, privacy storage, and data off-loading. Further, a prototype of the NuEdge system has been implemented to discover the possibilities and limitations of the prototype in a real-world scenario. The primary nudge goal of the system is to improve physical activity for inactive users. By gathering research on edge computing and smart nudging, combined with the implementation's observations, has edge computing's role in smart nudging been evaluated. Edge computing has significantly contributed to efficient data collection in a smart nudging system and lower latency for data transmissions. Future work should include a large-scale prototype and new technologies like 5G to investigate the limitations of edge device capabilities such as power consumption, storage, and computational power

Semantics-Empowered Big Data Processing with Applications

We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. We organize our research around the Five Vs of Big Data, where four of the Vs are harnessed to produce the fifth V - value. To handle the challenge of Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle the challenge of Variety, we resort to the use of semantic models and annotations of data so that much of the intelligent processing can be done at a level independent of heterogeneity of data formats and media. To handle the challenge of Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize relevant new concepts, entities and facts. To handle Veracity, we explore the formalization of trust models and approaches to glean trustworthiness. The above four Vs of Big Data are harnessed by the semantics-empowered analytics to derive value for supporting practical applications transcending physical-cyber-social continuum

Federated Learning on Edge Sensing Devices: A Review

The ability to monitor ambient characteristics, interact with them, and derive information about the surroundings has been made possible by the rapid proliferation of edge sensing devices like IoT, mobile, and wearable devices and their measuring capabilities with integrated sensors. Even though these devices are small and have less capacity for data storage and processing, they produce vast amounts of data. Some example application areas where sensor data is collected and processed include healthcare, environmental (including air quality and pollution levels), automotive, industrial, aerospace, and agricultural applications. These enormous volumes of sensing data collected from the edge devices are analyzed using a variety of Machine Learning (ML) and Deep Learning (DL) approaches. However, analyzing them on the cloud or a server presents challenges related to privacy, hardware, and connectivity limitations. Federated Learning (FL) is emerging as a solution to these problems while preserving privacy by jointly training a model without sharing raw data. In this paper, we review the FL strategies from the perspective of edge sensing devices to get over the limitations of conventional machine learning techniques. We focus on the key FL principles, software frameworks, and testbeds. We also explore the current sensor technologies, properties of the sensing devices and sensing applications where FL is utilized. We conclude with a discussion on open issues and future research directions on FL for further studie

Towards a service-oriented architecture for a mobile assistive system with real-time environmental sensing

Dalian Key Lab. of Smart Medical and Healthcare, Computer Science Department, Dalian University, China,With the growing aging population, age-related diseases have increased considerably over the years. In response to these, ambient assistive living (AAL) systems are being developed and are continually evolving to enrich and support independent living. While most researchers investigate robust activity recognition (AR) techniques, this paper focuses on some of the architectural challenges of the AAL systems. This work proposes a system architecture that fuses varying software design patterns and integrates readily available hardware devices to create wireless sensor networks for real-time applications. The system architecture brings together the service-oriented architecture (SOA), semantic web technologies, and other methods to address some of the shortcomings of the preceding system implementations using off-the-shelf and open source components. In order to validate the proposed architecture, a prototype is developed and tested positively to recognize basic user activities in real time. The system provides a base that can be further extended in many areas of AAL systems, including composite AR