Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929522293604352 |
|---|---|
| author | Yang, Zhongqi Azimi, Iman Jafarlou, Salar Labbaf, Sina Nguyen, Brenda Qureshi, Hana Marcotullio, Christopher Borelli, Jessica L. Dutt, Nikil Rahmani, Amir M. |
| author_facet | Yang, Zhongqi Azimi, Iman Jafarlou, Salar Labbaf, Sina Nguyen, Brenda Qureshi, Hana Marcotullio, Christopher Borelli, Jessica L. Dutt, Nikil Rahmani, Amir M. |
| contents | The adverse effects of loneliness on both physical and mental well-being are profound. Although previous research has utilized mobile sensing techniques to detect mental health issues, few studies have utilized state-of-the-art wearable devices to forecast loneliness and estimate the physiological manifestations of loneliness and its predictive nature. The primary objective of this study is to examine the feasibility of forecasting loneliness by employing wearable devices, such as smart rings and watches, to monitor early physiological indicators of loneliness. Furthermore, smartphones are employed to capture initial behavioral signs of loneliness. To accomplish this, we employed personalized machine learning techniques, leveraging a comprehensive dataset comprising physiological and behavioral information obtained during our study involving the monitoring of college students. Through the development of personalized models, we achieved a notable accuracy of 0.82 and an F-1 score of 0.82 in forecasting loneliness levels seven days in advance. Additionally, the application of Shapley values facilitated model explainability. The wealth of data provided by this study, coupled with the forecasting methodology employed, possesses the potential to augment interventions and facilitate the early identification of loneliness within populations at risk. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00020 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings Yang, Zhongqi Azimi, Iman Jafarlou, Salar Labbaf, Sina Nguyen, Brenda Qureshi, Hana Marcotullio, Christopher Borelli, Jessica L. Dutt, Nikil Rahmani, Amir M. Signal Processing Machine Learning The adverse effects of loneliness on both physical and mental well-being are profound. Although previous research has utilized mobile sensing techniques to detect mental health issues, few studies have utilized state-of-the-art wearable devices to forecast loneliness and estimate the physiological manifestations of loneliness and its predictive nature. The primary objective of this study is to examine the feasibility of forecasting loneliness by employing wearable devices, such as smart rings and watches, to monitor early physiological indicators of loneliness. Furthermore, smartphones are employed to capture initial behavioral signs of loneliness. To accomplish this, we employed personalized machine learning techniques, leveraging a comprehensive dataset comprising physiological and behavioral information obtained during our study involving the monitoring of college students. Through the development of personalized models, we achieved a notable accuracy of 0.82 and an F-1 score of 0.82 in forecasting loneliness levels seven days in advance. Additionally, the application of Shapley values facilitated model explainability. The wealth of data provided by this study, coupled with the forecasting methodology employed, possesses the potential to augment interventions and facilitate the early identification of loneliness within populations at risk. |
| title | Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2410.00020 |