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SLICE (Simulated Location-based Identification of Compulsive Events)

The SLICE (Simulated Location-based Identification of Compulsive Events) study was a semi-controlled feasibility study investigating the potential of wearable devices and indoor localization to detect routine and repetitive activity patterns. The study consisted of a one-hour session in which participants engaged in both naturalistic and protocol-driven activities within a semi-controlled multi room residential lab environment. It was approved by the ethics commission of the University of Potsdam (Approval No.: 38/2022).

IMU, UWB, wearables, HAR

Abstract:

The feasibility study was conducted at the Hasso Plattner Institute in Potsdam, Germany, and was designed to replicate everyday living conditions. The environment consisted of a kitchen, bathroom, main room, and hallways, and was designed to support the collection of high-resolution, multimodal sensor data.

The main session lasted approximately one hour, with the total time commitment per participant (including setup and debriefing) being around two hours. During the one-hour recording, participants were left alone in the residential lab to minimize the Hawthorne effect and to create a realistic everyday setting. They were instructed to engage in natural activities such as reading, preparing food, or working on a laptop, thereby generating baseline or NULL data. At the same time, they were asked to perform a specified number of simulated compulsive-like activities, namely handwashing, table cleaning, and door checking, at self-chosen times during the session. Each behavior was performed in two distinct ways to represent routine and compulsive-like patterns.

Data collector: Kristina Kirsten

Affiliations: Hasso Plattner Institute

Contact person / department: Kristina Kirsten, Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Germany

Study timeframe: December 2024

Number of study participants: 12

DOI: https://doi.org/10.5281/zenodo.17610331

Resource type: Dataset

Data modalities: IMU, BLE UWB Beacons

Amount of sensors: 2 × IMU, 5 × Beacon

Wearable / sensor manufacturers: Movella Inc., Estimote, Inc.

Data variables:
3-axis angular velocity, 3-axis euler angles, 3-axis free acceleration, distance estimation

Data variable units: °/s (degrees per second), m/s^2, ° (degrees), meters

Sampling frequencies: 60 Hz for IMU sensors, around 8 Hz for UWB BLE Beacons

Data inconsistencies / errors:
For Participant 4 the right wrist IMU stopped during the recording, hence the IMU data is incomplete.

Dataset size: 1.5 GB

File format: CSV

License: Creative Commons Attribution 4.0 International

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