Automatic chronic pain assessment and pain intensity estimation has been attracting growing attention due to its widespread applications. One of the prevalent issues in automatic pain analysis is inadequate balanced expert-labelled data for pain estimation. This work proposes an anomaly detection based network addressing one of the existing limitations of automatic pain assessment. The evaluation of the network is performed on pain intensity estimation and protective behaviour estimation tasks from body movements in the EmoPain Challenge dataset. The EmoPain dataset consists of body part based sensor data for both the tasks. The proposed network, PLAAN (Pain Level Assessment with Anomaly-detection based Network), is a lightweight LSTM-DNN network which considers features based on sensor data as the input and predicts intensity level of pain and presence or absence of protective behaviour in chronic low back pain patients. Joint training considering body movement patterns, such as exercise type, corresponding to pain exhibition as a label improves the performance of the network. However, contrary to perception, protective behaviour rather exists sporadically alongside pain in the EmoPain dataset. This induces yet another complication in accurate estimation of protective behaviour. This problem is resolved by incorporating anomaly detection in the network. A detailed comparison of different networks with varied features is outlined in the paper, presenting a significant improvement with the final proposed anomaly detection based network.

由于其广泛的应用，自动慢性疼痛评估和疼痛强度评估已引起越来越多的关注。自动疼痛分析中普遍存在的问题之一是用于疼痛估计的平衡专家标记的数据不足。这项工作提出了一种基于异常检测的网络，以解决自动疼痛评估的现有局限性之一。对网络的评估是根据EmoPain Challenge数据集中人体运动的疼痛强度估算和保护行为估算任务执行的。EmoPain数据集由两个任务的基于身体部位的传感器数据组成。拟议的网络PLAAN（基于基于异常检测的网络的疼痛程度评估），是一种轻量级的LSTM-DNN网络，该网络将基于传感器数据的特征视为输入，并预测慢性腰痛患者的疼痛强度水平以及保护行为的存在与否。结合身体活动方式（例如运动类型）进行联合训练，以疼痛表现为标签来改善网络的性能。但是，与感知相反，在EmoPain数据集中，保护性行为偶尔与疼痛并存。这在准确估计保护行为方面引起了另一个复杂性。通过将异常检测合并到网络中，可以解决此问题。本文概述了具有不同功能的不同网络的详细比较，对最终提出的基于异常检测的网络提出了重大改进。