Geneva School of Economics and Management, University of Geneva, 1200 Geneva, Switzerland.
United Nations Secretary General's Envoy on Technology, New York, NY 10017, USA.
Sensors (Basel). 2024 Sep 29;24(19):6298. doi: 10.3390/s24196298.
Recent studies on machine learning have shown the potential to provide new methods with which to assess pain through the measurement of signals associated with physiologic responses to pain detected by wearables. We conducted a prospective pilot study to evaluate the real-world feasibility of using an AI-enabled wearable system for pain assessment with elderly patients with dementia and impaired communication.
Sensor data were collected from the wearables, as well as observational data-based conventional everyday interventions. We measured the adherence, completeness, and quality of the collected data. Thereafter, we evaluated the most appropriate classification model for assessing the detectability and predictability of pain.
A total of 18 patients completed the trial period, and 10 of them had complete sensor and observational datasets. We extracted 206 matched records containing a 180 min long data segment from the sensor's dataset. The final dataset comprised 153 subsets labelled as moderate pain and 53 labelled as severe pain. After noise reduction, we compared the recall and precision performances of 14 common classification algorithms. The light gradient-boosting machine (LGBM) classifier presented optimal values for both performances.
Our findings tended to show that electrodermal activity (EDA), skin temperature, and mobility data are the most appropriate for pain detection.
最近的机器学习研究表明，通过可穿戴设备检测到的与生理疼痛反应相关的信号测量，有望提供新的疼痛评估方法。我们进行了一项前瞻性试点研究，以评估使用人工智能支持的可穿戴系统对患有痴呆症和沟通障碍的老年患者进行疼痛评估的实际可行性。
从可穿戴设备以及基于观察数据的常规日常干预中收集传感器数据。我们测量了收集数据的依从性、完整性和质量。然后，我们评估了最适合评估疼痛可检测性和可预测性的分类模型。
共有 18 名患者完成了试验期，其中 10 名患者有完整的传感器和观察数据集。我们从传感器数据集提取了 206 个包含 180 分钟长数据段的匹配记录。最终数据集包括 153 个标记为中度疼痛的子集和 53 个标记为重度疼痛的子集。经过降噪后，我们比较了 14 种常见分类算法的召回率和精度性能。轻梯度提升机（LGBM）分类器在这两个性能方面均表现出最佳值。
我们的研究结果表明，皮肤电活动（EDA）、皮肤温度和活动数据最适合用于疼痛检测。
