# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 1 Introduction  
A flight accident results in significant casualties and substantial financial losses [1]. Human factors contribute to approximately 75% of all accident causes [2]. Among these, aviation maintenance errors account for about 12-15% [3] since maintenance workers sometimes perform a variety of tasks within a limited timeframe with stress and fatigue [4]. However, a larger proportion is due to pilot operational issues [2]. The pilot operation issues that cause accidents mainly fall into two categories: One is the failure to complete standard operating procedures (SOPs) within their allowable operational time window (AOTW) [5]. Sometimes, the AOTW is simply too short for the pilot to complete the SOPs. In other cases, high workload impedes the pilots' ability to complete them within the AOTW. The second category is because of pilot operation errors [6]. A significant cause of these two types of situations is the high workload of pilots, which can significantly slow a pilot's reaction, hampering their ability to finish the procedures within the prescribed time limits [7]. It can also increase the probability of pilots making errors [8]. However, it is also worth noting that despite human errors often beingblamed for many accidents, pilots frequently intervene in a timely manner to ensure flight safety and do so far more often than they cause accidents under high workload [9]. If real-time detection of pilot workload is achievable, the data can be analyzed to identify trends and patterns in pilot workload under various flight conditions [10]. By examining data from periods of high workload, specific conditions and key causes of elevated workload can be identified. These insights can then be leveraged to redesign aircraft crew and maintenance operation procedures [11]. For example, this process can help in avoiding cascading high workloads to reduce accidents. Moreover, the human-machine interfaces (HMIs) in modern aircraft systems are increasingly digital and complex [12], which increases the pilot workload and poses potential safety risks. Based on the real-time detection of pilot workload, we can support modifications to cockpit HMIs to improve their designs [13]. Additionally, we can provide comprehensive metrics to help aviation regulatory bodies establish standards and guidelines, such as setting the requirements for cockpit configuration and the minimum crew [14]. At present, the main methods for detecting workload can be categorized into three types: subjective assessment, task performance measurement, and physiological measurement. Both subjective assessment and task performance measurement are post-evaluation methods [15] which cannot meet the real-time detection requirements for pilot workload. Physiological measurement utilizes physiological parameters to assess the workload of individuals while performing tasks. Studies [16; 17; 18] have shown that this method can effectively monitor pilots' mental health by tracking various physiological indicators, making it relatively objective and capable of real-time detection. However, the physiological devices commonly used in previous studies [17], such as electrode-type electroencephalography (EEG), electrocardiography (ECG), and galvanic skin response (GSR), are often equipped with invasive or patch-based components that may interfere with participants' performance. In contrast to these devices that cause interference, there exists a category of detection devices, such as telemetry eye trackers [19], in-ear EEG [20], and textile-based sensors [21]. These are characterized by low detection interference with pilot operations, thus making the workload data collection more authentic. In addition, in studies using physiological measurements for workload detection, most experiments evaluate the average workload throughout the entire process based on the overall task difficulty after the task is completed [22; 23]. Actually, during flight, the workload of pilots varies continuously, showing significant differences across different flight phases [24].