BLE-GSpeed: A New BLE-Based Dataset to Estimate User Gait Speed

Abstract

To estimate the user gait speed can be crucial in many topics, such as health care systems, since the presence of difficulties in walking is a core indicator of health and function in aging and disease. Methods for non-invasive and continuous assessment of the gait speed may be key to enable early detection of cognitive diseases such as dementia or Alzheimer’s disease. Wearable technologies can provide innovative solutions for healthcare problems. Bluetooth Low Energy (BLE) technology is excellent for wearables because it is very energy efficient, secure, and inexpensive. In this paper, the BLE-GSpeed database is presented. The dataset is composed of several BLE RSSI measurements obtained while users were walking at a constant speed along a corridor. Moreover, a set of experiments using a baseline algorithm to estimate the gait speed are also presented to provide baseline results to the research community.

A novel method for in-home Gait Speed estimation in Health Monitoring Using Bluetooth Low Energy

“…The continuous blue line is the predicted RSSI using (1), the red circles are the raw RSSI measurements, and the black points are the filtered RSSI. The numerical derivative, dr i is computed using the difference between consecutive elements, as shown in (7). Theoretically, there must only be measurements with the same frequency as the BLE beacon's transmission frequency; however, some consecutive measurements may have a minimal increment in time.…”

“…To test the proposed method the BLE-GSpeed database [7] is used. This database provides raw RSSI BLE measurements from weareble devices and real time GS measurements.…”

“…To select the parameters of ( 1) the RSSI measurements with the same timestamp as the ultrasonic sensor detections and the distances between the sensors and the beacons, depicted in Fig of [7] were used. The before mentioned data was adjusted to (1) using the least square error method.…”

“…The Internet of Things (IoT) orientation of BLE makes its design inexpensive compared with other systems and non-intrusive because it works with wearable commercial devices already familiar to all users, like smartphones and smartwatches. Our method is tested using the BLE-GSpeed database [7], which contains BLE RSSI measurements from different smartwatches and persons while walking through a straight walk. The experimental data can be found in the Zenodo repository [8].…”

Data from Smartphones and Wearables

“…In contrast to other similar works, the authors exploited commercial beacons' features to broadcast beacon advertisements in six independent slots with different transmission parameters and analyzed their impact on the positioning accuracy. The same commercial beacons were used in [6], where BLE-GSpeed is introduced to estimate gait speed through BLE RSSI values. Many different devices and actors were involved in this dataset to better document a wide variety of gait speeds and walking styles.…”