Dataset Information

A dataset for room level indoor localization using a smart home in a box.

ABSTRACT: An annotated dataset of measurements obtained using the EurValve Smart Home In a Box (SHIB) rehabilitation monitoring system is presented. The SHiB is a low cost and easily deployable kit designed to collect data from a wrist-worn wearable in a home environment. The data presented is intended to evaluate room level indoor localization methods. The wearable device registers tri-axial accelerometer measurements which are sampled and transmitted as the payload of a Bluetooth Low Energy (BLE) packet. Four receiving gateways, each placed in a different room throughout a typical residential house, extract the accelerometer data and determine a Received Signal Strength Indicator (RSSI) for each received BLE packet. RSSI values can represent propagation losses due to distance or shadowing between the wearable transmitter and the gateway receiver. The dataset is presented in two parts. The first is composed of four calibration or training sequences, carried out by ten participants to offer ground truth calibrations for four rooms in the house. We refer to the calibration phase as the steps taken to gather training data. The calibration procedure was designed to be as straight-forward as possible, to allow a participant to adequately train the SHiB system without supervision. Ten participants each carried out a straight forward calibration procedure once, with four participants carrying out the calibration twice, on different occasions. One participant carried out the calibration on a third occasion. The second part of the data consists of a free-living experiment that was carried out over a period of five and a half hours starting at 7.37?a.m. Of this, one and a half hours of measurements are recorded within a room containing a gateway, where one participant carried out activities of daily living while their ground-truth location was accurately annotated within each room with a gateway present. The calibration data can be used as a training scheme and the living data as a test scenario. The dataset can be found at https://github.com/rymc/a-dataset-for-indoor-localization-using-a-smart-home-in-a-box.

SUBMITTER: McConville R

PROVIDER: S-EPMC6356000 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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A dataset for room level indoor localization using a smart home in a box.

McConville Ryan R Byrne Dallan D Craddock Ian I Piechocki Robert R Pope James J Santos-Rodriguez Raul R

Data in brief 20190119

An annotated dataset of measurements obtained using the EurValve Smart Home In a Box (SHIB) rehabilitation monitoring system is presented. The SHiB is a low cost and easily deployable kit designed to collect data from a wrist-worn wearable in a home environment. The data presented is intended to evaluate room level indoor localization methods. The wearable device registers tri-axial accelerometer measurements which are sampled and transmitted as the payload of a Bluetooth Low Energy (BLE) packet ...[more]

PMID: 30740491

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Project description:BackgroundThe Internet of Things (IoT) has become integrated into everyday life, with devices becoming permanent fixtures in many homes. As countries face increasing pressure on their health care systems, smart home technologies have the potential to support population health through continuous behavioral monitoring.ObjectiveThis scoping review aims to provide insight into this evolving field of research by surveying the current technologies and applications for in-home health monitoring.MethodsPeer-reviewed papers from 2008 to 2021 related to smart home technologies for health care were extracted from 4 databases (PubMed, Scopus, ScienceDirect, and CINAHL); 49 papers met the inclusion criteria and were analyzed.ResultsMost of the studies were from Europe and North America. The largest proportion of the studies were proof of concept or pilot studies. Approximately 78% (38/49) of the studies used real human participants, most of whom were older females. Demographic data were often missing. Nearly 60% (29/49) of the studies reported on the health status of the participants. Results were primarily reported in engineering and technology journals. Almost 62% (30/49) of the studies used passive infrared sensors to report on motion detection where data were primarily binary. There were numerous data analysis, management, and machine learning techniques employed. The primary challenges reported by authors were differentiating between multiple participants in a single space, technology interoperability, and data security and privacy.ConclusionsThis scoping review synthesizes the current state of research on smart home technologies for health care. We were able to identify multiple trends and knowledge gaps-in particular, the lack of collaboration across disciplines. Technological development dominates over the human-centric part of the equation. During the preparation of this scoping review, we noted that the health care research papers lacked a concrete definition of a smart home, and based on the available evidence and the identified gaps, we propose a new definition for a smart home for health care. Smart home technology is growing rapidly, and interdisciplinary approaches will be needed to ensure integration into the health sector.

Project description:BackgroundClimate change, driven by human activity, is rapidly changing our environment and posing an increased risk to human health. Local governments must adapt their cities and prepare for increased periods of extreme heat and ensure that marginalized populations do not suffer detrimental health outcomes. Heat warnings traditionally rely on outdoor temperature data which may not reflect indoor temperatures experienced by individuals. Smart thermostats could be a novel and highly scalable data source for heat wave monitoring.ObjectiveThe objective of this study was to explore whether smart thermostats can be used to measure indoor temperature during a heat wave and identify houses experiencing indoor temperatures above 26°C.MethodsWe used secondary data-indoor temperature data recorded by ecobee smart thermostats during the Quebec heat waves of 2018 that claimed 66 lives, outdoor temperature data from Environment Canada weather stations, and indoor temperature data from 768 Quebec households. We performed descriptive statistical analyses to compare indoor temperatures differences between air conditioned and non-air conditioned houses in Montreal, Gatineau, and surrounding areas from June 1 to August 31, 2018.ResultsThere were significant differences in indoor temperature between houses with and without air conditioning on both heat wave and non-heat wave days (P<.001). Households without air conditioning consistently recorded daily temperatures above common indoor temperature standards. High indoor temperatures persisted for an average of 4 hours per day in non-air conditioned houses.ConclusionsOur findings were consistent with current literature on building warming and heat retention during heat waves, which contribute to increased risk of heat-related illnesses. Indoor temperatures can be captured continuously using smart thermostats across a large population. When integrated with local heat health action plans, these data could be used to strengthen existing heat alert response systems and enhance emergency medical service responses.

Dataset Information

A dataset for room level indoor localization using a smart home in a box.

Publications

A dataset for room level indoor localization using a smart home in a box.

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