sensor-data

QuestDB is a high performance, open-source, time-series database

Parser for GPMF™ formatted telemetry data used within GoPro® cameras.

The Most Advanced Time Series Platform

MotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope) (PMC Journal) (IoTDI'19)

VeloView performs real-time visualization and easy processing of live captured 3D LiDAR data from Velodyne sensors (Alpha Prime™, Puck™, Ultra Puck™, Puck Hi-Res™, Alpha Puck™, Puck LITE™, HDL-32, HDL-64E). Runs on Windows, Linux and MacOS. This repository is a mirror of https://gitlab.kitware.com/LidarView/VeloView-Velodyne.

Notes on Machine Learning on edge for embedded/sensor/IoT uses

Log data from an ESP8266 device directly to Google Sheets without a third party service. Log sensor data, send data by pressing a button, and receive data from a Google spreadsheet. (NodeMCU, Wemos D1 mini, Adafruit Feather HUZZAH, etc)

RF24BLE is the library that makes an nrf24L01+ chip (1$) into a BLE advertising beacon and can be used for LOW payload advertising like sensor data etc.

Bresser 5-in-1/6-in-1/7-in-1 868 MHz Weather Sensor Radio Receiver for Arduino based on CC1101, SX1276/RFM95W, SX1262 or LR1121

Web server using a Raspberry Pi and DHT22 sensor to graph the humidity and temperature in my apartment over time.

Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

An iOS framework that provides Mobile Sensing to your apps.

Method for Predicting failures in Equipment using Sensor data. Sensors mounted on devices like IoT devices, Automated manufacturing like Robot arms, Process monitoring and Control equipment etc., collect and transmit data on a continuous basis which is Time stamped.

BEEP - Open source bee monitoring - App/API/Measurement system

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.

An Android framework that provides Mobile Sensing to your apps.

Package for analyzing human motion data (e.g. PA, gait)

IoT workshop based on ESP8266, a DHT11/22 and neopixel RGB LED