Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 411215, India.
Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune 411215, India.
J Healthc Eng. 2021 Oct 28;2021:9356452. doi: 10.1155/2021/9356452. eCollection 2021.
In the current information age, the human lifestyle has become more knowledge-oriented, leading to sedentary employment. This has given rise to a number of health and mental disorders. Mental wellness is one of the most neglected, however crucial, aspects of today's fast-paced world. Mental health issues can, both directly and indirectly, affect other sections of human physiology and impede an individual's day-to-day activities and performance. However, identifying the stress and finding the stress trend for an individual that may lead to serious mental ailments is challenging and involves multiple factors. Such identification can be achieved accurately by fusing these multiple modalities (due to various factors) arising from a person's behavioral patterns. Specific techniques are identified in the literature for this purpose; however, very few machine learning-based methods are proposed for such multimodal fusion tasks. In this work, a multimodal AI-based framework is proposed to monitor a person's working behavior and stress levels. We propose a methodology for efficiently detecting stress due to workload by concatenating heterogeneous raw sensor data streams (e.g., face expressions, posture, heart rate, and computer interaction). This data can be securely stored and analyzed to understand and discover personalized unique behavioral patterns leading to mental strain and fatigue. The contribution of this work is twofold: firstly, proposing a multimodal AI-based strategy for fusion to detect stress and its level and, secondly, identifying a stress pattern over a period of time. We were able to achieve 96.09% accuracy on the test set in stress detection and classification. Further, we were able to reduce the stress scale prediction model loss to 0.036 using these modalities. This work can prove important for the community at large, specifically those working sedentary jobs, to monitor and identify stress levels, especially in current times of COVID-19.
在当前的信息时代，人类的生活方式变得更加以知识为导向，导致人们久坐不动地工作。这引发了许多健康和心理障碍。然而，心理健康是当今快节奏世界中最被忽视但至关重要的方面之一。心理健康问题既可以直接，也可以间接地影响人体生理的其他部分，并阻碍个人的日常活动和表现。然而，识别个体可能导致严重精神疾病的压力并发现压力趋势是具有挑战性的，涉及多个因素。通过融合个人行为模式产生的这些多种模式（由于各种因素）可以准确地识别这些压力。为此，文献中确定了特定的技术；但是，针对这种多模式融合任务，提出的基于机器学习的方法很少。在这项工作中，提出了一种基于人工智能的多模态框架来监测个人的工作行为和压力水平。我们提出了一种通过串联异构原始传感器数据流（例如，面部表情、姿势、心率和计算机交互）来有效检测因工作量引起的压力的方法。可以安全地存储和分析这些数据，以了解和发现导致精神紧张和疲劳的个性化独特行为模式。这项工作的贡献有两个方面：首先，提出了一种基于人工智能的多模式融合策略来检测压力及其水平，其次，识别一段时间内的压力模式。我们能够在压力检测和分类方面在测试集上实现 96.09%的准确率。此外，我们能够使用这些模式将压力量表预测模型的损失降低到 0.036。这项工作对于广大社区，特别是那些从事久坐工作的人来说非常重要，可以帮助他们监测和识别压力水平，尤其是在当前 COVID-19 时期。
