Indoor Location IoT Analytics “in the wild”: Active and Healthy Ageing Cases

Abstract

This paper presents an accurate indoor localisation approach to provide context aware support for Activities of Daily Living. This paper explores the use of contemporary wearable technology (Google Glass) to facilitate a unique first-person view of the occupants environment. Machine vision techniques are then employed to determine an occupant's location via environmental object detection within their field of view. Specifically, the video footage is streamed to a server where object recognition is performed using the Oriented Features from Accelerated Segment Test and Rotated Binary Robust Independent Elementary Features algorithm with a K-Nearest Neighbour matcher to match the saved keypoints of the objects to the scene. To validate the approach, an experimental setup consisting of three ADL routines, each containing at least ten activities, ranging from drinking water to making a meal were considered. Ground truth was obtained from manually annotated video data and the approach was subsequently benchmarked against a common method of indoor localisation that employs dense sensor placement. The paper presents the results from these experiments, which highlight the feasibility of using off-the-shelf machine vision algorithms to determine indoor location based on data input from wearable video-based sensor technology. The results show a recall, precision, and F-measure of 0.82, 0.96, and 0.88 respectively. This method provides additional secondary benefits such as first person tracking within the environment and lack of required sensor interaction to determine occupant location.

IoT of active and healthy ageing: cases from indoor location analytics in the wild

“…The work presented in this paper extends and introduces the authors' previous work on density based clustering on indoor (location) transitions [20] [21] in real seniors' homes. Within this work, the CAC framework and Indoor Analytics Client are implemented over a long-term data capture period of about 12 months.…”

“…Healthy Ageing Living Lab (Thess-AHALL), an adherent member of the European Network of Living Labs located in the Lab of Medical Physics in the Aristotle University of Thessaloniki in Greece [20], as well as in real seniors homes [21]. A software client subscribes, listens, collects and applies a density clustering algorithm on real IoT indoor position data, streamed at the time of occurrence, analyzes the data and exposes the so called IoT analytics results through a Rest API feeding back the environment with meaningful information.…”

Implementation of Universal Health Management and Monitoring System in Resource-Constrained Environment Based on Internet of Things

“…In the context of industrial intelligence manufacturing, the maintenance of industrial production equipment has become extraordinarily difficult, owing to the changing working environment, complex principles and operation processes, and demand for high stability [20][21][22][23]. Marques et al [24] optimized the neural network model with genetic algorithm (GA) to predict the failure of industrial production equipment, achieved the collection, transmission, storage, and processing of the monitoring data on industrial production equipment based on industrial IoT (IIoT), provided detailed designs of software and hardware, and completed the full-scale test on the monitoring platform.…”