Laura Fiorini

A Cloud Robotics Solution to Improve Social Assistive Robots for Active and Healthy Aging

Technological innovation in robotics and ICT represents an effective solution to tackle the challenge of providing social sustainable care services for the ageing population. The recent introduction of cloud technologies is opening new opportunities for the provisioning of advanced robotic services based on the cooperation of a number of connected robots, smart environments and devices improved by the huge cloud computational and storage capability. In this context, this paper aims to investigate and assess the potentialities of a cloud robotic system for the provisioning of assistive services for the promotion of active and healthy ageing. The system comprised two different smart environments, located in Italy and Sweden, where a service robot is connected to a cloud platform for the provisioning of localization based services to the users. The cloud robotic services were tested in the two realistic environments to assess the general feasibility of the solution and demonstrate the ability to provide assistive location based services in a multiple environment framework. The results confirmed the validity of the solution but also suggested a deeper investigation on the dependability of the communication technologies adopted in such kind of systems.

Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets

Recognition of activities of daily living plays an important role in monitoring elderly people and helping caregivers in controlling and detecting changes in daily behaviors. Thanks to the miniaturization and low cost of Microelectromechanical systems (MEMs), in particular of Inertial Measurement Units, in recent years body-worn activity recognition has gained popularity. In this context, the proposed work aims to recognize nine different gestures involved in daily activities using hand and wrist wearable sensors. Additionally, the analysis was carried out also considering different combinations of wearable sensors, in order to find the best combination in terms of unobtrusiveness and recognition accuracy. In order to achieve the proposed goals, an extensive experimentation was performed in a realistic environment. Twenty users were asked to perform the selected gestures and then the data were off-line analyzed to extract significant features. In order to corroborate the analysis, the classification problem was treated using two different and commonly used supervised machine learning techniques, namely Decision Tree and Support Vector Machine, analyzing both personal model and Leave-One-Subject-Out cross validation. The results obtained from this analysis show that the proposed system is able to recognize the proposed gestures with an accuracy of 89.01% in the Leave-One-Subject-Out cross validation and are therefore promising for further investigation in real life scenarios.

Preliminary Findings of the AALIANCE2 Ambient Assisted Living Roadmap

The AALIANCE2 Project, funded by the European Commission’s ICT Programme within the 7th Framework Programme, aims at identifying the research priorities in the Ambient Assisted Living (AAL) field in Europe and worldwide for the next decades. One of the main objectives of this Project is the development of an AAL Roadmap and Strategic Research Agenda (SRA) that, starting from the needs of the elderly and caregivers, describes the possible next generation of AAL service scenarios, the necessary key enabling technologies (KETs) and the technological, legal and economic requirements necessary for the implementation of the proposed AAL systems. Some of these new AAL scenarios show how technologies, such as robotics and ICT solutions, could be used in senior citizens’ daily life activities to maintain their independence and to stay healthy and active in society. At the same time, other scenarios propose new approaches and solutions for caregivers to efficiently support old persons and optimize their work. The Roadmap and the Strategic Research Agenda finally present the future technological challenges to developing the proposed service solutions. This paper provides a short overview of the preliminary version of the AALIANCE2 Roadmap.

Design of cloud robotic services for senior citizens to improve independent living in multiple environments

The paper proposed a cloud robotic solution for the healthcare management of senior citizens, to demonstrate the opportunity to remotely provide continuous assistive robotic services to a number of seniors regardless to their position in the monitored environment. In particular, a medication reminding, a remote home monitoring and an user indoor localization service were outsourced in the cloud and provided to the robots, users and caregivers on request. The proposed system was composed of a number of robotic agents distributed over two smart environments: a flat at the Domocasa Lab (Peccioli, IT) and a condominium at the Angen site of the Orebro science park (Orebro, SE). The cloud acquired data from remote smart environments and enabled the local robots to provide advanced assistive services to a number of users. The proposed smart environments were able to collect raw data for the environmental monitoring and the localization of the users by means of wireless sensors, and provide such data to the cloud. On the cloud, specific algorithms improved the local robots, by providing event scheduling to accomplish assistive services and situation awareness on the users position and environments’ status. The indoor user localization service, was provided by means of commercial and ad-hoc sensors distributed over the environments and a sensor fusion algorithm on the cloud. The entire cloud solution was evaluated in terms of Quality of Service (QoS) to estimate the effectiveness of the architecture.

Enabling personalised medical support for chronic disease management through a hybrid robot-cloud approach

Information and Communication Technology and personal robots could play a fundamental role in efficiently managing chronic diseases and avoiding improper medications. They could support senior citizens with reminders, thus promoting their independent living and quality of life, especially in the presence of several chronic diseases (multimorbidity). In this context, this article proposes a service model for personalised medical support that is able to provide adequate healthcare service by means of a hybrid robot-cloud approach. This service was quantitatively and qualitatively tested to assess the technical feasibility and user acceptability level of the service. The service was tested with 23 older people (65–86 years) in the DomoCasa Lab (Italy). This study demonstrated the feasibility of the proposed hybrid cloud solution and the usability and acceptability were positively evaluated thus confirming the ability to utilise these innovative technologies for active and healthy ageing.

Enabling Global Robot Navigation Based on a Cloud Robotics Approach

In the future, social robots will permeate our daily life. An autonomous robot that has to move among different buildings needs to manage huge amount of data, as a consequence it is clear that the configuration of the navigation system becomes hard to manage. This paper presents a system, based on a cloud robotics paradigm, conceived to allow autonomous robots to navigate in indoor environment, which are not known a priori. The environment is divided into sub-maps and all the necessary information and the topological representation of the world, are stored into a remote cloud infrastructure. By means of specific environmental tags, composed of a set of ARTags and QR codes, the robot gets the access to the cloud service and it is able to update its navigation configuration in a dynamic and automatic way. Experiments have been conducted in order to choose an appropriate marker dimension and to demonstrate the feasibility of the proposed procedure.

Design of Cloud Robotic Services for Senior Citizens to Improve Independent Living and Personal Health Management

A cloud robotics solution was designed and initially tested with a mobile robotic platform and a smart environment, in order to provide health-care management services to senior citizens and improve their independent living. The solution was evaluated in terms of Quality of Service (QoS) and tested in the realistic scenario of the DomoCasa Living Lab, Peccioli, Italy. In particular, a medication reminding service, a remote home monitoring and a user indoor localization algorithm were outsourced in the cloud and provided to the robots, users and carers. The system acquired data from a smart environment and addressed the robot to the user for service delivery. Experiments showed a service’s Reliability of Response at least of the 0.04 % and a Time of Response of the same order of magnitude of the processing time required by the user localization algorithm.

Two-person activity recognition using skeleton data

Human activity recognition is an important and active field of research having a wide range of applications in numerous fields including ambient-assisted living (AL). Although most of the researches are focused on the single user, the ability to recognise two-person interactions is perhaps more important for its social implications. This study presents a two-person activity recognition system that uses skeleton data extracted from a depth camera. The human actions are encoded using a set of a few basic postures obtained with an unsupervised clustering approach. Multiclass support vector machines are used to build models on the training set, whereas the X-means algorithm is employed to dynamically find the optimal number of clusters for each sample during the classification phase. The system is evaluated on the Institute of Systems and Robotics (ISR) - University of Lincoln (UoL) and Stony Brook University (SBU) datasets, reaching overall accuracies of 0.87 and 0.88, respectively. Although the results show that the performances of the system are comparable with the state of the art, recognition improvements are obtained with the activities related to health-care environments, showing promise for applications in the AL realm.

Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data

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.

Use Case Evaluation of a Cloud Robotics Teleoperation System (Short Paper)

The paper describes a generic Cloud Robotics teleoperation system which allows to control in real-time a robot (connected with a 4G network) having its video stream as feedback. The proposed system relies on the Azure Cloud Platform and on recent web technologies. Particularly, we present an use case experiment in which an operator in Slovakia controls a robot situated in Italy in order to evaluate its realtime feasibility. We test the system to assess its performances providing the throughput value of the communication and the average delay between consecutive received packets on both robot and teleoperation side. Additionally, regarding the video streaming, we test several packet sizes to establish a suitable image quality. The results show how the chosen technology allows to have real-time performances in terms of video and velocity commands streaming.