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Sean Noran's Projects

gesture-analysis icon gesture-analysis

This application is intended to analyze raw accelerometer and gyroscope data collected via the My-Gestures Android application. It includes gesture- and session-level visualization and classification tools.

imu-data-collection icon imu-data-collection

See the My-Gestures repository. This is the equivalent application for Microsoft Band.

iot-smart-home-simulation icon iot-smart-home-simulation

Distributed Operations Assignment 2 : This is a distributed system of IoT devices, including sensors and smart devices that communicate through a central gateway. Events are ordered using clock synchronization as well as logical clocks.

metawearlogging icon metawearlogging

A sample application demonstrating how to log sensor data on a Metawear device and download it later to the phone.

metawearmultipleconnections icon metawearmultipleconnections

A sample application demonstrating that the phone can connect to up to 4 Metawear devices and stream accelerometer data from each simultaneously without any issues.

mhlclient icon mhlclient

Library which allows Android applications to send sensor data to the MHealth Lab server.

mil icon mil

Gesture recognition under sparse fine-grained labels using Multi-Instance Learning

misvm icon misvm

Multiple-Instance Support Vector Machines

my-activities icon my-activities

This is the starter code for CS390MB Mobile Health Sensing. My Activities is a daily health monitoring application, which tracks personal fitness, heart rate, sociability and frequent locations. The framework is provided; be ambitious!

my-gestures icon my-gestures

My Gestures is a research tool intended to collect easily labelled sensor data from an Android wearable device. The application currently supports Accelerometer/Gyroscope data labelled by voice commands.

pb-detection icon pb-detection

Pill Bottle Detector Research with Professor Deepak Ganesan using the Microsoft Band and BLE Accelerometers

prepare icon prepare

Improving medication adherence for HIV+ and at-risk patients. Available for Android and Android Wear, PrEPare tracks medication adherence by detecting pill intake gestures from accelerometer and gyroscope streams. It also manages a medication regiment and sets daily reminders. Adherence data, personal details and medication schedules are also stored on a server and accessible by primary care providers.

prepare_analysis icon prepare_analysis

This is the server-side code for pill intake detection. It includes scripts for processing, segmenting and classifying data by hand-to-mouth gesture and offers real-time analytics using the MHLClient library.

webrtc-experiment icon webrtc-experiment

WebRTC audio/video conferencing, audio/video recording, screen-sharing, file-sharing, WebRTC data-channels, pre-recorded media streaming, part-of-screen sharing, broadcasting, hangout; WebRTC signaling: SIP, WebSockets, socket.io; peer-to-peer and peer-to-server based WebRTC Experiments. Group text-chat and file-transferring; audio-only calls; and much more! WebRTC libraries like RecordRTC, RTCPeerConnection, RTCMultiConnection, RTCall, and DataChannel.js

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