This repository provides the codes and data used in our paper "Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art", where we implement and evaluate several state-of-the-art approaches, ranging from handcrafted-based methods to convolutional neural networks.
Classifying the physical activities performed by a user based on accelerometer and gyroscope sensor data collected by a smartphone in the user’s pocket. The activities to be classified are: Standing, Sitting, Stairsup, StairsDown, Walking and Cycling.
Package for analyzing human motion data (e.g. PA, gait)
Companion servers in Node and Python for the PhonePi Sensor Streamer apps.
Improving Human Activity Recognition through Self-training with Unlabeled Data
Gateway application for forwarding Sensor Data and Sensor Events from sources including Serial, BLE, TCP/IP.
Companion servers for Sensor Stream App. Stream sensor data, audio and images from your phone to an open source server running on your PC/Raspberry Pi in real-time over your local network.
Data set and source code used in "Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study."
A Python library for reading, writing and visualizing the OMEGA Format, targeted towards storing reference and perception data in the automotive context on an object list basis with a focus on an urban use case.
Sensor data aggregation tool for any numerical sensor data. Robust and privacy-friendly.
Source code for the Gait Recognition using LSTM, presented in the paper "Multi-model Long Short-term Memory Network for Gait Recognition using Window-based Data Segment"
A python package for analyzing environmental sensor's data
Demonstration how to build a Management Web interface to interact with the FPGA fabric and change the FPGA configuration with the Django Framework
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition
Code for "ObscureNet: Learning Attribute-invariant Latent Representation for Anonymizing Sensor Data", ACM/IEEE IoTDI 2021
Python API client for https://portal.onboarddata.io
Self-Supervised Few-Shot Time-series Segmentation for Activity Recognition, TMC 2022
Package for preprocessing Paderborn Bearing dataset
A Sensor Data Displaying/Streaming Android App built in Python with Beeware tools.
AI for social good /Risk assessment
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