Trajectory based Primitive Events for learning and recognizing Activity

International audienceThis paper proposes a framework to recognize and classify loosely constrained activities with minimal supervision. The framework use basic trajectory information as input and goes up to video interpretation. The work reduces the gap between low-level information and semantic interpretation, building an intermediate layer composed Primitive Events. The proposed representation for primitive events aims at capturing small meaningful motions over the scene with the advantage of been learnt in an unsupervised manner. We propose the modelling of an activity using Primitive Events as the main descriptors. The activity model is built in a semi-supervised way using only real tracking data. Finally we validate the descriptors by recognizing and labelling modelled activities in a home-care application dataset

Employing Information and Communications Technologies in Homes and Cities for the Health and Well-Being of Older People

YesHe X and Sheriff RE (Eds.) Employing ICT in Homes and Cities for the Health and Well-Being of Older People. Workshop Proceedings of ICT4HOP’16. 15-17 Aug 2016. Sichuan University, Chengdu, China.British Council, Researcher Links, Newton Fund, NSF

Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services

With Machine Learning (ML) services now used in a number of mission-critical human-facing domains, ensuring the integrity and trustworthiness of ML models becomes all-important. In this work, we consider the paradigm where cloud service providers collect big data from resource-constrained devices for building ML-based prediction models that are then sent back to be run locally on the intermittently-connected resource-constrained devices. Our proposed solution comprises an intelligent polynomial-time heuristic that maximizes the level of trust of ML models by selecting and switching between a subset of the ML models from a superset of models in order to maximize the trustworthiness while respecting the given reconfiguration budget/rate and reducing the cloud communication overhead. We evaluate the performance of our proposed heuristic using two case studies. First, we consider Industrial IoT (IIoT) services, and as a proxy for this setting, we use the turbofan engine degradation simulation dataset to predict the remaining useful life of an engine. Our results in this setting show that the trust level of the selected models is 0.49% to 3.17% less compared to the results obtained using Integer Linear Programming (ILP). Second, we consider Smart Cities services, and as a proxy of this setting, we use an experimental transportation dataset to predict the number of cars. Our results show that the selected model's trust level is 0.7% to 2.53% less compared to the results obtained using ILP. We also show that our proposed heuristic achieves an optimal competitive ratio in a polynomial-time approximation scheme for the problem

Distributed Computing and Monitoring Technologies for Older Patients

This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions

Exploring homecare for people living with dementia using an ethnographic approach

Background: Most people living with dementia prefer to remain in their own homes. Support from homecare services can enable this, yet homecare workers often receive limited training and support. Aim: To learn and understand from the experiences of homecare workers how they can be better trained and supported in their role, and how they can support independence in people living with dementia. Methods: I conducted a systematic review of observation methods used to study homecare. This informed the design of my ethnographic study, comprising participant observations with 16 homecare workers and 17 clients living with dementia, and 82 qualitative interviews with people living with dementia, family carers, homecare staff and health and social care professionals. I triangulated the data and thematically analysed the findings. I used my findings to inform the coproduced NIDUS-Professional training and support intervention. Findings: The value of homecare relationships and the significance of the home were two prominent, overarching findings. Relationships between homecare workers, clients, family carers and other health and social care professionals were often complex to navigate, yet were key to meeting the needs of people living with dementia. Care provision in the home setting transitioned the environment into a hybrid space between the clients’ domestic space and the homecare workers’ workplace. Conclusion: In highlighting the significance of the home for people living with dementia, I posit the importance of responsive, person-centred and home-centred care. Relational and emotional aspects of homecare are central to workers’ training and support. Establishing interdependent, collaborative relationships with clients can enable meaningful decision-making and active participation in daily tasks. Recognising and valuing homecare workers’ position amongst multidisciplinary dementia-care services, alongside managerial and peer support, may reduce some of the role’s associated challenges. Moving towards professionalisation of the homecare workforce is a clear direction for future research, policy and practice