# A State-of-the-Art Review of Computational Models for Analyzing Longitudinal Wearable Sensor Data in Healthcare

arXiv: https://arxiv.org/abs/2407.21665 · HTML: https://mlsanity.com/paper/2407.21665/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 86 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Multi-task Self-Supervised Learning for Human Activity Detection](https://mlsanity.com/paper/1907.11879/) (2019)
- [Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review](https://mlsanity.com/paper/2209.15137/) (2022)
- [Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones](https://mlsanity.com/paper/2209.09692/) (2022)
- [Sensor-Based Estimation of Dim Light Melatonin Onset (DLMO) Using Features of Two Time Scales](https://mlsanity.com/paper/1908.07483/) (2019)
- [Circadian Rhythms are Not Captured Equal: Exploring Circadian Metrics Extracted by Different Computational Methods from Smartphone Accelerometer and GPS Sensors in Daily Life Tracking](https://mlsanity.com/paper/2107.04135/) (2021)

## Built on by (most cited first, 1 of 1)

- [Personalized Counterfactual Framework: Generating Potential Outcomes from Wearable Data](https://mlsanity.com/paper/2508.14432/) (2025)

## Abstract (arXiv)

Wearable devices are increasingly used as tools for biomedical research, as the continuous stream of behavioral and physiological data they collect can provide insights about our health in everyday contexts. Long-term tracking, defined in the timescale of months of year, can provide insights of patterns and changes as indicators of health changes. These insights can make medicine and healthcare more predictive, preventive, personalized, and participative (The 4P's). However, the challenges in modeling, understanding and processing longitudinal data are a significant barrier to their adoption in research studies and clinical settings. In this paper, we review and discuss three models used to make sense of longitudinal data: routines, rhythms and stability metrics. We present the challenges associated with the processing and analysis of longitudinal wearable sensor data, with a special focus on how to handle the different temporal dynamics at various granularities. We then discuss current limitations and identify directions for future work. This review is essential to the advancement of computational modeling and analysis of longitudinal sensor data for pervasive healthcare.