Multi-Sensors for Human Activity Recognition.

Human activity recognition (HAR) has made significant progress in recent years, with growing applications in various domains, and the emergence of wearable and ambient sensors has provided new opportunities in the field [...].

Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis.

In this paper, we demonstrate the potential of a knowledge-driven framework to improve the efficiency and effectiveness of care through remote and intelligent assessment. More specifically, we present a rule-based approach to detect health related problems from wearable lifestyle sensor data that add clinical value to take informed decisions on follow-up and intervention. We use OWL 2 ontologies as the underlying knowledge representation formalism for modelling contextual information and high-level concepts and relations among them. The conceptual model of our framework is defined on top of existing modelling standards, such as SOSA and WADM, promoting the creation of interoperable knowledge graphs. On top of the symbolic knowledge graphs, we define a rule-based framework for infusing expert knowledge in the form of SHACL constraints and rules to recognise patterns, anomalies and situations of interest based on the predefined and stored rules and conditions. A dashboard visualizes both sensor data and detected events to facilitate clinical supervision and decision making. Preliminary results on the performance and scalability are presented, while a focus group of clinicians involved in an exploratory research study revealed their preferences and perspectives to shape future clinical research using the framework.

Smart integration of sensors, computer vision and knowledge representation for intelligent monitoring and verbal human-computer interaction.

The details presented in this article revolve around a sophisticated monitoring framework equipped with knowledge representation and computer vision capabilities, that aims to provide innovative solutions and support services in the healthcare sector, with a focus on clinical and non-clinical rehabilitation and care environments for people with mobility problems. In contemporary pervasive systems most modern virtual agents have specific reactions when interacting with humans and usually lack extended dialogue and cognitive competences. The presented tool aims to provide natural human-computer multi-modal interaction via exploitation of state-of-the-art technologies in computer vision, speech recognition and synthesis, knowledge representation, sensor data analysis, and by leveraging prior clinical knowledge and patient history through an intelligent, ontology-driven, dialogue manager with reasoning capabilities, which can also access a web search and retrieval engine module. The framework's main contribution lies in its versatility to combine different technologies, while its inherent capability to monitor patient behaviour allows doctors and caregivers to spend less time collecting patient-related information and focus on healthcare. Moreover, by capitalising on voice, sensor and camera data, it may bolster patients' confidence levels and encourage them to naturally interact with the virtual agent, drastically improving their moral during a recuperation process.

Combining RSSI and Accelerometer Features for Room-Level Localization.

The continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors. Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. A common service offered by IoT systems is the estimation of a person's position in indoor spaces, which is quite often achieved with the exploitation of the Received Signal Strength Indication (RSSI). Localization tasks with the goal to locate the room are actually classification problems. Motivated by a current project, where there is the need to locate a missing child in crowded spaces, we intend to test the added value of using an accelerometer along with RSSI for room-level localization and assess the performance of ensemble learning methods. We present here the results of this preliminary approach of the early and late fusion of RSSI and accelerometer features in room-level localization. We further test the performance of the feature extraction from RSSI values. The classification algorithms and the fusion methods used to predict the room were evaluated using different protocols applied to a public dataset. The experimental results revealed better performance of the RSSI extracted features, while the accelerometer's individual performance was poor and subsequently affected the fusion results.

Long-Term Impact of Intelligent Monitoring Technology on People with Cognitive Impairment: An Observational Study.

BACKGROUND: Interactive smart home systems are particularly useful for people with cognitive impairment. OBJECTIVE: To investigate the long-term effects of Assistive Technology (AT) combined with tailored non-pharmacological interventions for people with cognitive impairment. METHODS: 18 participants (12 with mild cognitive impairment and 6 with Alzheimer's disease) took part in the study that we evenly allocated in one of three groups: 1) experimental group (EG), 2) control group 1 (CG1), and 3) control group 2 (CG2). EG received the system installed at home for 4 to 12 months, during which they received tailored non-pharmacological interventions according to system observations. CG1 received tailored interventions for the same period, but only according to state-of-the-art self-reporting methods. Finally, CG2 neither had a system installation nor received interventions. All groups underwent neuropsychological assessment before and after the observational period. RESULTS: After several months of continuously monitoring at home and deployment of tailored interventions, the EG showed statistically significant improvement in cognitive function, compared to the CG1 and CG2. Moreover, EG participants, who received the sensor-based system, have shown improvement in domains such as sleep quality and daily activity, as measured by the multi-sensor system. In addition, the feedback collected from the participants concludes that the long-term use of the multi-sensor system by people with cognitive impairment can be both feasible and beneficial. CONCLUSION: Deploying a sensor-based system at real home settings of people with cognitive limitations living alone and maintaining its use long-term is not only possible, but also beneficial for clinical decision making in order to tackle cognitive, functional, and behavioral related problems.

A Novel and Intelligent Home Monitoring System for Care Support of Elders with Cognitive Impairment.

BACKGROUND: Assistive technology, in the form of a smart home environment, is employed to support people with dementia. OBJECTIVES: To propose a system for continuous and objective remote monitoring of problematic daily living activity areas and design personalized interventions based on system feedback and clinical observations for improving cognitive function and health-related quality of life. METHODS: The assistive technology of the proposed system, including wearable, sleep, object motion, presence, and utility usage sensors, was methodically deployed at four different home installations of people with cognitive impairment. Detection of sleep patterns, physical activity, and activities of daily living, based on the collected sensor data and analytics, was available at all times through comprehensive data visualization solutions. Combined with clinical observation, targeted psychosocial interventions were introduced to enhance the participants' quality of life and improve their cognitive functions and daily functionality. Meanwhile, participants and their caregivers were able to visualize a reduced set of information tailored to their needs. RESULTS: Overall, paired-sample t-test analysis of monitored qualities revealed improvement for all participants in neuropsychological assessment. Moreover, improvement was detected from the beginning to the end of the trial, in physical condition and in the domains of sleep. Detecting abnormalities via the system, for example in sleep quality, such as REM sleep, has proved to be critical to assess current status, drive interventions, and evaluate improvements in a reliable manner. CONCLUSION: It has been proved that the proposed system is suitable to support clinicians to reliably drive and evaluate clinical interventions toward quality of life improvement of people with cognitive impairment.

Semantic Event Fusion of Different Visual Modality Concepts for Activity Recognition.

Combining multimodal concept streams from heterogeneous sensors is a problem superficially explored for activity recognition. Most studies explore simple sensors in nearly perfect conditions, where temporal synchronization is guaranteed. Sophisticated fusion schemes adopt problem-specific graphical representations of events that are generally deeply linked with their training data and focused on a single sensor. This paper proposes a hybrid framework between knowledge-driven and probabilistic-driven methods for event representation and recognition. It separates semantic modeling from raw sensor data by using an intermediate semantic representation, namely concepts. It introduces an algorithm for sensor alignment that uses concept similarity as a surrogate for the inaccurate temporal information of real life scenarios. Finally, it proposes the combined use of an ontology language, to overcome the rigidity of previous approaches at model definition, and a probabilistic interpretation for ontological models, which equips the framework with a mechanism to handle noisy and ambiguous concept observations, an ability that most knowledge-driven methods lack. We evaluate our contributions in multimodal recordings of elderly people carrying out IADLs. Results demonstrated that the proposed framework outperforms baseline methods both in event recognition performance and in delimiting the temporal boundaries of event instances.