Abstract
This repository offers smart-home wearable accelerometer and Radio Signal Strength Indicator (RSSI) data acquired : 1) with low-cost hardware; 2) with high-resolution location annotations; 3) from four UK homes. The data are intended to evaluate RSSI-based indoor localisation methods with activity measurements provided from a user-worn wearable device. A wrist worn accelerometer records activity signatures which are relayed to a number of receiving Access Points (AP) placed throughout the building. Upon reception of a packet, each AP measures the RSSI of the received radio signal and timestamps the accelerometer measurements. Location labels are recorded automatically using a small camera which registers fiducial floor tags as the participant carries out their normal routines in a natural way. Approximately 14 h of annotated wearable measurements are provided. A scripted fingerprint measurement is provided along with several unscripted natural living recordings, where the participant carried out a number of daily household activities which are annotated, where possible, throughout. Codes are provided to access the data and to replicate the ground-truthing procedure.
Highlights
Background & SummaryLow-cost, networked, smart-home technologies can be used to alleviate the burden faced by national health services, freeing up valuable resources for patients requiring acute treatments
Each static node measures the Received Signal Strength (RSS) Indicator (RSSI) which represents the received RF power of an individual packet of wearable data as it propagates from the wearable RF device
Eight APs were deployed in residence A as the space was significantly smaller than the other houses
Summary
Low-cost, networked, smart-home technologies can be used to alleviate the burden faced by national health services, freeing up valuable resources for patients requiring acute treatments. Popleteev et al.[10] provided accurate high-resolution locations using laser methods for a dataset of ambient RF signal data (ambient FM, GSM and television signals) in a single residential apartment These measurements were taken when the user was stationary as free-living or motion experiments cannot be annotated due to the significant weight and form-factor of the ground-truthing equipment. A Python module is present which detects Bluetooth Low Energy (BLE) advertising packets on any AP running a linux-based operating system These data can be used to benchmark indoor localisation methods which incorporate RSSI and/or accelerometer measurements from a wrist worn wearable device[5]. The recorded wearable accelerometer and RSSI measurements can be annotated to a particular participant position within the house
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