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Passive mobile sensing and psychological traits for large scale mood prediction

Passive mobile sensing and psychological traits for large scale mood prediction
Passive mobile sensing and psychological traits for large scale mood prediction


Experience sampling has long been the established method to sample people's mood in order to assess their mental state. Smartphones start to be used as experience sampling tools for mental health state as they accompany individuals during their day and can therefore gather in-the-moment data. However, the granularity of the data needs to be traded off with the level of interruption these tools introduce. As a consequence the data collected with this technique is often sparse. This has been obviated by the use of passive sensing in addition to mood reports, however, this adds additional noise.

In this paper we show that psychological traits collected through one-off questionnaires combined with passively collected sensing data (movement from the accelerometer and noise levels from the microphone) can be used to detect individuals whose general mood deviates from the common relaxed characteristic of the general population. By using the reported mood as a classification target we show how to design models that depend only on passive sensors and one-off questionnaires, without bothering users with tedious experience sampling. We validate our approach by using a large dataset of mood reports and passive sensing data collected in the wild with tens of thousands of participants, finding that the combination of these modalities achieves the best classification performance, and that passive sensing yields a +5% boost in accuracy. We also show that sensor data collected for a week performs better than single days for this task. We discuss feature extraction techniques and appropriate classifiers for this kind of multimodal data, as well as overfitting shortcomings of using deep learning to handle static and dynamic features. We believe these findings have significant implications for mobile health applications that can benefit from the correct modeling of passive sensing along with extra user metadata.
272-281
ACM
Spathis, Dimitris
0352ccc2-a97c-4273-998a-0bc8546612fa
Servia-Rodriguez, Sandra
cb942d57-9954-4e9e-9bcf-bf2309d774b0
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Mascolo, Cecilia
78286d55-4fd6-41c3-bd6b-4a41baf46694
Rentfrow, Jason
a80783c0-af6f-4164-88ca-9a7e71c3a90e
Spathis, Dimitris
0352ccc2-a97c-4273-998a-0bc8546612fa
Servia-Rodriguez, Sandra
cb942d57-9954-4e9e-9bcf-bf2309d774b0
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Mascolo, Cecilia
78286d55-4fd6-41c3-bd6b-4a41baf46694
Rentfrow, Jason
a80783c0-af6f-4164-88ca-9a7e71c3a90e

Spathis, Dimitris, Servia-Rodriguez, Sandra, Farrahi, Katayoun, Mascolo, Cecilia and Rentfrow, Jason (2019) Passive mobile sensing and psychological traits for large scale mood prediction. In PervasiveHealth'19 Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM. pp. 272-281 . (doi:10.1145/3329189.3329213).

Record type: Conference or Workshop Item (Paper)

Abstract



Experience sampling has long been the established method to sample people's mood in order to assess their mental state. Smartphones start to be used as experience sampling tools for mental health state as they accompany individuals during their day and can therefore gather in-the-moment data. However, the granularity of the data needs to be traded off with the level of interruption these tools introduce. As a consequence the data collected with this technique is often sparse. This has been obviated by the use of passive sensing in addition to mood reports, however, this adds additional noise.

In this paper we show that psychological traits collected through one-off questionnaires combined with passively collected sensing data (movement from the accelerometer and noise levels from the microphone) can be used to detect individuals whose general mood deviates from the common relaxed characteristic of the general population. By using the reported mood as a classification target we show how to design models that depend only on passive sensors and one-off questionnaires, without bothering users with tedious experience sampling. We validate our approach by using a large dataset of mood reports and passive sensing data collected in the wild with tens of thousands of participants, finding that the combination of these modalities achieves the best classification performance, and that passive sensing yields a +5% boost in accuracy. We also show that sensor data collected for a week performs better than single days for this task. We discuss feature extraction techniques and appropriate classifiers for this kind of multimodal data, as well as overfitting shortcomings of using deep learning to handle static and dynamic features. We believe these findings have significant implications for mobile health applications that can benefit from the correct modeling of passive sensing along with extra user metadata.

Full text not available from this repository.

More information

Accepted/In Press date: May 2019
Published date: 20 May 2019
Venue - Dates: 13th EAI International Conference on Pervasive Computing Technologies for Healthcare, Italy, 2019-05-20 - 2019-05-23

Identifiers

Local EPrints ID: 431903
URI: http://eprints.soton.ac.uk/id/eprint/431903
PURE UUID: 32495ce3-6bff-4d0b-95dc-5c12e9672186

Catalogue record

Date deposited: 21 Jun 2019 16:30
Last modified: 02 Jun 2020 16:32

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