Technology and Innovative Services

Cavallo

Dario

et al. 2017

Sensors

The goal of this study is to address two major issues that undermine the large scale deployment of smart home sensing solutions in people’s homes. These include the costs associated with having to install and maintain a large number of sensors, and the pragmatics of annotating numerous sensor data streams for activity classification. Our aim was therefore to propose a method to describe individual users’ behavioural patterns starting from unannotated data analysis of a minimal number of sensors and a ”blind” approach for activity recognition. The methodology included processing and analysing sensor data from 17 older adults living in community-based housing to extract activity information at different times of the day. The findings illustrate that 55 days of sensor data from a sensor configuration comprising three sensors, and extracting appropriate features including a “busyness” measure, are adequate to build robust models which can be used for clustering individuals based on their behaviour patterns with a high degree of accuracy (>85%). The obtained clusters can be used to describe individual behaviour over different times of the day. This approach suggests a scalable solution to support optimising the personalisation of care by utilising low-cost sensing and analysis. This approach could be used to track a person’s needs over time and fine-tune their care plan on an ongoing basis in a cost-effective manner.

Section: Introductionmentioning

confidence: 99%

Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data

Cavallo

Dario

et al. 2017

Sensors

“…can be demonstrated. If elderly people can live independently as long as possible, this will have a financial impact on the health system but also have an impact on the roles in health care: older persons and their carers will be more independent and autonomous [37]. In this sense, the results of ACCRA project can be used to forecast health system sustainability.…”

Section: Discussionmentioning

confidence: 99%

ACCRA Project: Agile Co-Creation for Robots and Aging

Lecture Notes in Electrical Engineering

Limosani

Sancarlo

et al. 2019

The mission of ACCRA (Agile Co-Creation for Robots and Aging) is to enable the development of advanced ICT Robotics based solutions for extending active and healthy aging in daily life by defining, developing and demonstrating an agile co-creation development process. ACCRA project consists of three robotic applications which aim to promote the independent living by means of personal mobility application, to support the daily life management thanks to housework application and to promote conversation rehabilitation tailored on personal attitude by means of dedicated software programme. Additionally, ACCRA project will be designed and developed on open source framework (i.e. ROS, FIWARE, universAAL and Rospex) to promote the interoperability among scientific community.

“…Often service models include strict coperations between different stakeholders, such as end-users, caregivers, service providers, healthcare organizations, developers, manifactures, governative entities, etc., which require to be harmoniously integrated and coordinated between them. ICT and robotic technologies have the potential to improve the quality of services, optimizing care processes and reinforcing the benefits for end-users [41,46]. * Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

Combining wearable physiological and inertial sensors with indoor user localization network to enhance activity recognition

Bonaccorsi

Betti

et al. 2018

AIS

Abstract. Thanks to the pervasiveness of smart technologies, researchers could aggregate data and investigate user's activities thus to deliver personalized home-care services. Activity recognition system have been widely developed, however some challenges still need to be addressed. This paper presents a system where information on body movement, vital signs and user indoor location are aggregated to improve the activity recognition. The system was tested in a realistic environment with a total of 3279 instances acquired from ten healthy users. These results encouraged the use of this approach in activity recognition applications, indeed, the overall accuracy values are satisfactory increased.