Toward End-to-end Prediction of Future Wellbeing using Deep Sensor Representation Learning
Toward End-to-end Prediction of Future Wellbeing using Deep Sensor Representation Learning
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DOI：
10.1109/aciiw.2019.8925072
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Boning Li;Han Yu;Akane Sano
中科院分区：
文献类型：
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作者：
Boning Li;Han Yu;Akane Sano
可穿戴传感器可以捕获连续、高分辨率的生理和行为数据，可用于开发早期健康和福祉检测，并形成早期预警、干预和推荐系统，以改善健康和福祉。我们构建并评估了一个端到端的健康预测框架，该框架将原始可穿戴传感器数据传输到基于无监督自动编码器的表示学习模型和有监督的健康回归模型中。我们使用可穿戴传感器数据集和从大学生（N=252 中总共 6391 天）收集的健康标签来训练和评估该框架。可穿戴数据包括皮肤温度、皮肤电导和加速度；幸福感标签包括自我报告的警觉性、幸福感、精力、健康和冷静度，得分为 0 - 100。我们将框架的性能与基于手工制作的特征的幸福感回归模型的性能进行了比较。我们的结果表明，所提出的框架可以自动从当天的 24 小时多通道数据中提取特征，并预测第二天的健康得分，平均绝对误差为 14-16。这一结果表明使用端到端框架准确预测福祉的可能性，最终用于开发实时健康和福祉监测和干预系统。
Wearable sensors can capture continuous, high resolution physiological and behavioral data that can be utilized to develop early health and wellbeing detection and lead to early warning, intervention, and recommendation systems to improve health and wellbeing. We have built and evaluated an end-to-end wellbeing prediction framework that pipelines raw wearable sensor data into an unsupervised autoencoder-based representation learning model and a supervised wellbeing regression model. We trained and evaluated the framework using the wearable sensor dataset and wellbeing labels collected from college students (total 6391 days from N=252). Wearable data include skin temperature, skin conductance, and acceleration; the wellbeing labels include self-reported alertness, happiness, energy, health, and calmness scored 0 – 100. We compared the performance of our framework with the performance of wellbeing regression models based on hand-crafted features. Our results showed that the proposed framework can automatically extract features from the current day's 24-hour multi-channel data and predict wellbeing scores for next day with mean absolute errors of 14–16. This result shows the possibility of predicting wellbeing accurately using an end-to-end framework, ultimately for developing real-time health and wellbeing monitoring and intervention systems.