AI on the Move From : From On-Device to On-Multi-Device

On-Device AI is an emerging paradigm that aims to make devices more intelligent, autonomous and proactive by equipping them with machine and deep learning routines for robust decision making and optimal execution in devices' operations. On-Device intelligence promises the possibility of computing huge amounts of data close to its source, e.g., sensor and multimedia data. By doing so, devices can complement their counterpart cloud services with more sophisticated functionality to provide better applications and services. However, increased computational capabilities of smart devices, wearables and IoT devices along with the emergence of services at the Edge of the network are driving the trend of migrating and distributing computation between devices. Indeed, devices can reduce the burden of executing resource intensive tasks via collaborations in the wild. While several work has shown the benefits of an opportunistic collaboration of a device with others, not much is known regarding how devices can be organized as a group as they move together. In this paper, we contribute by analyzing how dynamic group organization of devices can be utilized to distribute intelligence on the moving Edge. The key insight is that instead of On-Device solutions complementing with cloud, dynamic groups can be formed to complement each other in an On-Multi-Device manner. Thus, we highlight the challenges and opportunities from extending the scope of On-Device AI from an egocentric view to a collaborative, multi-device view.Peer reviewe

NOVA mobility assistive system: Developed and remotely controlled with IOPT-tools

UID/EEA/00066/2020In this paper, a Mobility Assistive System (NOVA-MAS) and a model-driven development approach are proposed to support the acquisition and analysis of data, infrastructures control, and dissemination of information along public roads. A literature review showed that the work related to mobility assistance of pedestrians in wheelchairs has a gap in ensuring their safety on road. The problem is that pedestrians in wheelchairs and scooters often do not enjoy adequate and safe lanes for their circulation on public roads, having to travel sometimes side by side with vehicles and cars moving at high speed. With NOVA-MAS, city infrastructures can obtain information regarding the environment and provide it to their users/vehicles, increasing road safety in an inclusive way, contributing to the decrease of the accidents of pedestrians in wheelchairs. NOVA-MAS not only supports information dissemination, but also data acquisition from sensors and infrastructures control, such as traffic light signs. For that, it proposed a development approach that supports the acquisition of data from the environment and its control while using a tool framework, named IOPT-Tools (Input-Output Place-Transition Tools). IOPT-Tools support controllers’ specification, validation, and implementation, with remote operation capabilities. The infrastructures’ controllers are specified through IOPT Petri net models, which are then simulated using computational tools and verified using state-space-based model-checking tools. In addition, an automatic code generator tool generates the C code, which supports the controllers’ implementation, avoiding manual codification errors. A set of prototypes were developed and tested to validate and conclude on the feasibility of the proposals.publishersversionpublishe

ERICA: Enabling real-time mistake detection and corrective feedback for free-weights exercises

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

An analysis of the properties and the performance of WiFi RTT for indoor positioning in non-line-of-sight environments

Indoor positioning system based on WiFi Round-Trip Time (RTT) measurement is believed to deliver sub-metre level accuracy with trilateration, under ideal indoor conditions. However, the performance of WiFi RTT positioning in complex, non-line-of-sight environments re-mains a research challenge.To this end, this paper investigates the properties of WiFi RTT in several real-world indoor environments on heterogeneous smartphones. We present a large-scale real-world dataset containing both RTT and received signal strength (RSS) signal measures with correct ground-truth labels.Our results indicated that RTT fingerprinting system delivered an accuracy below 0.75 m which was 98% better than RSS fingerprinting and 166% better than RTT trilateration, which failed to deliver sub-metre accuracy as claimed

Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification using Data Mining Models and Methods

This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test

CAT S60 smartphone as a portable wound care device in home care

The purpose of this work is to study the suitability of the CAT S60 smartphone with built-in thermal camera to be used in self and home care to detect the risk level of wound appearance in advance. The purpose was to clarify different conditions where thermal imaging might act as a resource in detecting changes in limb circulation before visual signs even occur. The purpose is to detect early incipient tissue damage in foot usually occur in diabetic patients. Thermal images were acquired from voluntary domesticated elderly people. Thermal pictures from limbs of 3 persons were studied in order to find thermal differences indicating possible changes in limb circulation. Noteworthy thermal differences between limbs were found in elderly people. A smartphone having built-in thermal camera enables to detect plantar and limb thermal differences with a sufficient accuracy. This may support home monitoring for elderly people and thus reduce foot ulcers and possible foot amputations due to earlier detection and identification of harmful changes in limb circulation. Earlier detection of circulatory insufficiency via thermal imaging makes possible for nurses to intervene and enable medical assistance

Toward Massive Scale Air Quality Monitoring

Dangers associated with poor air quality are driving deployments of air quality monitoring technology. These deployments rely either on professional-grade measurement stations or a small number of low-cost sensors integrated into urban infrastructure. In this article, we present a research vision of real-time massive scale air quality sensing that integrates tens of thousands or even millions of air quality sensors to monitor air quality at fine spatial and temporal resolution. We highlight opportunities and challenges of our vision by discussing use cases, key requirements and reference technologies in order to establish a roadmap on how to realize this vision. We address the feasibility of our vision, introducing a testbed deployment in Helsinki, Finland, and carrying out controlled experiments that address collaborative and opportunistic sensor calibration, a key research challenge for our vision.Peer reviewe