# Activity Recognition and Prediction in Real Homes

## TL;DR

This paper explores activity recognition and prediction in real homes using sensor and depth video data, comparing probabilistic and LSTM methods, and demonstrating transfer learning and activity classification.

## Contribution

It introduces a comprehensive approach combining sensor and depth video data for activity prediction and recognition in real homes, including transfer learning techniques.

## Key findings

- LSTM models improve next event prediction accuracy.
- Transfer learning enables effective model deployment across apartments.
- Depth video analysis classifies four key activities with simple processing.

## Abstract

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its mean time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low-resolution depth video data from seven apartments, and classify four activities - no movement, standing up, sitting down, and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.