# Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning

arXiv: https://arxiv.org/abs/2305.20056 · HTML: https://mlsanity.com/paper/2305.20056/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 9 of 9)

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder](https://mlsanity.com/paper/1711.00614/) (2017)
- [Having a Bad Day? Detecting the Impact of Atypical Life Events Using Wearable Sensors](https://mlsanity.com/paper/2008.01723/) (2020)
- [Unsupervised non-parametric change point detection in quasi-periodic signals](https://mlsanity.com/paper/2002.02717/) (2020)
- [A Graph-constrained Changepoint Detection Approach for ECG Segmentation](https://mlsanity.com/paper/2004.13558/) (2020)
- [Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data](https://mlsanity.com/paper/1907.00749/) (2019)
- [Doubly-online changepoint detection for monitoring health status during sports activities](https://mlsanity.com/paper/2206.11578/) (2022)

## Abstract (arXiv)

Rare life events significantly impact mental health, and their detection in behavioral studies is a crucial step towards health-based interventions. We envision that mobile sensing data can be used to detect these anomalies. However, the human-centered nature of the problem, combined with the infrequency and uniqueness of these events makes it challenging for unsupervised machine learning methods. In this paper, we first investigate granger-causality between life events and human behavior using sensing data. Next, we propose a multi-task framework with an unsupervised autoencoder to capture irregular behavior, and an auxiliary sequence predictor that identifies transitions in workplace performance to contextualize events. We perform experiments using data from a mobile sensing study comprising N=126 information workers from multiple industries, spanning 10106 days with 198 rare events (<2%). Through personalized inference, we detect the exact day of a rare event with an F1 of 0.34, demonstrating that our method outperforms several baselines. Finally, we discuss the implications of our work from the context of real-world deployment.