Stress Tracker—Detecting Acute Stress From a Trackpad: Controlled Study

Background Stress is a risk factor associated with physiological and mental health problems. Unobtrusive, continuous stress sensing would enable precision health monitoring and proactive interventions, but current sensing methods are often inconvenient, expensive, or suffer from limited adherence. Prior work has shown the possibility to detect acute stress using biomechanical models derived from passive logging of computer input devices. Objective Our objective is to detect acute stress from passive movement measurements of everyday interactions on a laptop trackpad: (1) click, (2) steer, and (3) drag and drop. Methods We built upon previous work, detecting acute stress through the biomechanical analyses of canonical computer mouse interactions and extended it to study similar interactions with the trackpad. A total of 18 participants carried out 40 trials each of three different types of movement—(1) click, (2) steer, and (3) drag and drop—under both relaxed and stressed conditions. Results The mean and SD of the contact area under the finger were higher when clicking trials were performed under stressed versus relaxed conditions (mean area: P=.009, effect size=0.76; SD area: P=.01, effect size=0.69). Further, our results show that as little as 4 clicks on a trackpad can be used to detect binary levels of acute stress (ie, whether it is present or not). Conclusions We present evidence that scalable, inexpensive, and unobtrusive stress sensing can be done via repurposing passive monitoring of computer trackpad usage.

Stress Tracker—Detecting Acute Stress From a Trackpad: Controlled Study (Preprint)

BACKGROUND Stress is a risk factor associated with physiological and mental health problems. Unobtrusive, continuous stress sensing would enable precision health monitoring and proactive interventions, but current sensing methods are often inconvenient, expensive, or suffer from limited adherence. Prior work has shown the possibility to detect acute stress using biomechanical models derived from passive logging of computer input devices. OBJECTIVE Our objective is to detect acute stress from passive movement measurements of everyday interactions on a laptop trackpad: (1) <i>click</i>, (2) <i>steer</i>, and (3) <i>drag and drop</i>. METHODS We built upon previous work, detecting acute stress through the biomechanical analyses of canonical computer mouse interactions and extended it to study similar interactions with the trackpad. A total of 18 participants carried out 40 trials each of three different types of movement—(1) <i>click</i>, (2) <i>steer</i>, and (3) <i>drag and drop</i>—under both relaxed and stressed conditions. RESULTS The mean and SD of the contact area under the finger were higher when clicking trials were performed under stressed versus relaxed conditions (mean area: <i>P</i>=.009, effect size=0.76; SD area: <i>P</i>=.01, effect size=0.69). Further, our results show that as little as 4 clicks on a trackpad can be used to detect binary levels of acute stress (ie, whether it is present or not). CONCLUSIONS We present evidence that scalable, inexpensive, and unobtrusive stress sensing can be done via repurposing passive monitoring of computer trackpad usage.

Design and Application of a Novel Virtual Reality Navigational Technology (VRNChair)

This paper presents a novel virtual reality navigation (VRN) input device, called the VRNChair, offering an intuitive and natural way to interact with virtual reality (VR) environments. Traditionally, VR navigation tests are performed using stationary input devices such as keyboards or joysticks. However, in case of immersive VR environment experiments, such as our recent VRN assessment, the user may feel kinetosis (motion sickness) as a result of the disagreement between vestibular response and the optical flow. In addition, experience in using a joystick or any of the existing computer input devices may cause a bias in the accuracy of participant performance in VR environment experiments. Therefore, we have designed a VR navigational environment that is operated using a wheelchair (VRNChair). The VRNChair translates the movement of a manual wheelchair to feed any VR environment. We evaluated the VRNChair by testing on 34 young individuals in two groups performing the same navigational task with either the VRNChair or a joystick; also one older individual (55 years) performed the same experiment with both a joystick and the VRNChair. The results indicate that the VRNChair does not change the accuracy of the performance; thus removing the plausible bias of having experience using a joystick. More importantly, it significantly reduces the effect of kinetosis. While we developed VRNChair for our spatial cognition study, its application can be in many other studies involving neuroscience, neurorehabilitation, physiotherapy, and/or simply the gaming industry.