# Test-Retest Repeatability of Human Gestures in Manipulation Tasks
## 1 Introduction  
The concept of collaborative robotics, introduced by Industry 4.0 and reinforced by Industry 5.0, involves different levels of human-machine interaction. The most demanding case in terms of safety occurs if humans and robots simultaneously share the same workspace [1]. Two different approaches can be exploited to guarantee safety, the power and force limiting (PFL) and the speed and separation monitoring (SSM). PFL combines methods of passive safety design with an energy control of mobile robotic components, limiting force, torques and velocity. On the contrary, SSM imposes a protection distance between the robot and the operator, without specifying any limitations for the robotic system. In detail, when the operator is inside the shared workspace, SSM approach implies an online control of the robot to generate alternative paths without collisions. In this case, systems for the human motion tracking must be adopted to monitor the minimum distance. Moreover, algorithms for the collision avoidance have to be implemented to update the robot trajectory in real-time. Many research works [2; 3; 4; 5] have already exploited different technologies to track human motion and recognize human gestures in case of repetitive and controlled industrial tasks such as assembly [6] and pick-and-place [2]. However, the operators can perform abrupt gestures different from normal movements due to inattention and unexpected circumstances not directly related to the job task. When these situations occur, it is essential to detect abnormal gestures at their onset, avoiding collisions between the human and themachine while maintaining a high efficiency of execution [7]. Some research works have already studied the characteristics of abrupt movements, the methods for identifying their occurrence and their effects on the performance of task execution [8; 9; 10; 11]. However, attention devoted to optimizing the detection of abrupt movements as early as possible remains scant. Optical systems are not adequate for this purpose because they work at too low frequencies and they are not able to detect abrupt gestures as early as possible. The problem of frequency can be solved by adopting wearable magneto-inertial measurement units (MIMUs), which collect data from the triaxial accelerometer, gyroscope and magnetometer embedded in each sensor [12]. In addition, MIMUs also overcome other limits typical of optical systems such as occlusion, encumbrance, a limited range of acquisition volume, heavy weight and high cost. In contrast, the sensor fusion process performed for the estimation of MIMUs' orientation can produce problems related to the drift phenomenon and to ferromagnetic disturbances affecting magnetometer readings. Instead, the problem of the early detection of abrupt movements typical of optical systems can be approached through deep learning techniques, which are useful to extract and learn features directly from raw data and hence to instruct the machine about human motion [13; 14; 15]. To guarantee a high classification performance, deep learning neural networks need an initial training phase. Considering this scenario, repeatability of the gesture over time has to be verified to understand whether and after how long it is necessary to retrain the network. Therefore, a consistency evaluation referring to both the degree of correlation and the correspondence between measurements has to be assessed through a test-retest. The estimation of test-retest repeatability based on MIMUs' signals has been approached in different contexts such as the classification of construction activities in the industrial field [16], the biomechanical 3D investigation of gait [17], or the improvement of sport training load quantification [18]. However, since this approach has been poorly applied in the context of human-machine interaction, the aim of this work was to evaluate the test-retest repeatability in a typical industrial gesture.