Depression Recognition Using Daily Wearable-Derived Physiological Data
The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life...
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MDPI AG
2025-01-01
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| author | Xinyu Shui Hao Xu Shuping Tan Dan Zhang |
| author_facet | Xinyu Shui Hao Xu Shuping Tan Dan Zhang |
| author_sort | Xinyu Shui |
| collection | DOAJ |
| description | The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states. The present study leverages multimodal wristband devices to collect data from fifty-eight participants clinically diagnosed with depression during their normal daytime activities over six hours. Data collected include pulse wave, skin conductance, and triaxial acceleration. For comparison, we also utilized data from fifty-eight matched healthy controls from a publicly available dataset, collected using the same devices over equivalent durations. Our aim was to identify depressive individuals through the analysis of multimodal physiological measurements derived from wearable devices in daily life scenarios. We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. Utilizing a Random Forest algorithm, we distinguished depressive and non-depressive individuals with varying classification accuracies on data aggregated over 6 h, 2 h, 30 min, and 5 min segments, as 90.0%, 84.7%, 80.1%, and 76.0%, respectively. Our results demonstrate the feasibility of using daily wearable-derived physiological data for depression recognition. The achieved classification accuracies suggest that this approach could be integrated into clinical settings for the early detection and monitoring of depressive symptoms. Future work will explore the potential of these methods for personalized interventions and real-time monitoring, offering a promising avenue for enhancing mental health care through the integration of wearable technology. |
| format | Article |
| id | doaj-art-9fde80177d7747cba978823dad2eabfa |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
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| series | Sensors |
| spelling | doaj-art-9fde80177d7747cba978823dad2eabfa2025-01-24T13:49:22ZengMDPI AGSensors1424-82202025-01-0125256710.3390/s25020567Depression Recognition Using Daily Wearable-Derived Physiological DataXinyu Shui0Hao Xu1Shuping Tan2Dan Zhang3Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, ChinaBeijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, ChinaBeijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, ChinaDepartment of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, ChinaThe objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states. The present study leverages multimodal wristband devices to collect data from fifty-eight participants clinically diagnosed with depression during their normal daytime activities over six hours. Data collected include pulse wave, skin conductance, and triaxial acceleration. For comparison, we also utilized data from fifty-eight matched healthy controls from a publicly available dataset, collected using the same devices over equivalent durations. Our aim was to identify depressive individuals through the analysis of multimodal physiological measurements derived from wearable devices in daily life scenarios. We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. Utilizing a Random Forest algorithm, we distinguished depressive and non-depressive individuals with varying classification accuracies on data aggregated over 6 h, 2 h, 30 min, and 5 min segments, as 90.0%, 84.7%, 80.1%, and 76.0%, respectively. Our results demonstrate the feasibility of using daily wearable-derived physiological data for depression recognition. The achieved classification accuracies suggest that this approach could be integrated into clinical settings for the early detection and monitoring of depressive symptoms. Future work will explore the potential of these methods for personalized interventions and real-time monitoring, offering a promising avenue for enhancing mental health care through the integration of wearable technology.https://www.mdpi.com/1424-8220/25/2/567depressionwearable devicemultimodaldynamicautoregressive |
| spellingShingle | Xinyu Shui Hao Xu Shuping Tan Dan Zhang Depression Recognition Using Daily Wearable-Derived Physiological Data Sensors depression wearable device multimodal dynamic autoregressive |
| title | Depression Recognition Using Daily Wearable-Derived Physiological Data |
| title_full | Depression Recognition Using Daily Wearable-Derived Physiological Data |
| title_fullStr | Depression Recognition Using Daily Wearable-Derived Physiological Data |
| title_full_unstemmed | Depression Recognition Using Daily Wearable-Derived Physiological Data |
| title_short | Depression Recognition Using Daily Wearable-Derived Physiological Data |
| title_sort | depression recognition using daily wearable derived physiological data |
| topic | depression wearable device multimodal dynamic autoregressive |
| url | https://www.mdpi.com/1424-8220/25/2/567 |
| work_keys_str_mv | AT xinyushui depressionrecognitionusingdailywearablederivedphysiologicaldata AT haoxu depressionrecognitionusingdailywearablederivedphysiologicaldata AT shupingtan depressionrecognitionusingdailywearablederivedphysiologicaldata AT danzhang depressionrecognitionusingdailywearablederivedphysiologicaldata |