A machine-learning method isolating changes in wrist kinematics that identify age-related changes in arm movement.

Normal aging often results in an increase in physiological tremors and slowing of the movement of the hands, which can impair daily activities and quality of life. This study, using lightweight wearable non-invasive sensors, aimed to detect and identify age-related changes in wrist kinematics and response latency. Eighteen young (ages 18-20) and nine older (ages 49-57) adults performed two standard tasks with wearable inertial measurement units on their wrists. Frequency analysis revealed 5 kinematic variables distinguishing older from younger adults in a postural task, with best discrimination occurring in the 9-13 Hz range, agreeing with previously identified frequency range of age-related tremors, and achieving excellent classifier performance (0.86 AUROC score and 89% accuracy). In a second pronation-supination task, analysis of angular velocity in the roll axis identified a 71 ms delay in initiating arm movement in the older adults. This study demonstrates that an analysis of simple kinematic variables sampled at 100 Hz frequency with commercially available sensors is reliable, sensitive, and accurate at detecting age-related increases in physiological tremor and motor slowing. It remains to be seen if such sensitive methods may be accurate in distinguishing physiological tremors from tremors that occur in neurological diseases, such as Parkinson's Disease.

Bigger is really better: Resolution of conflicting behavioural evidence for semantic size bias in a lexical decision task.

Previous literature has indicated conflicting results regarding a response time bias favouring words indicating large real-world objects (RWO) over words indicating small RWO during a lexical decision task. This study aimed to replicate an original experiment and, expanding on it, disentangle possible alternatives for why this effect is sometimes observed and sometimes not. The same methods as the original study were followed, and the results were inconsistent with all previously published findings. Although no significant difference was observed for response time, the findings indicated a significant difference in accuracy and inverse efficiency scores such that "large" words were recognised significantly more accurately than "small" words. After examining several linguistic dimensions that may also contribute to response time, statistical models accounting for these dimensions yielded a significant and increased effect size for the response time size rating of words in our sample from the United States. Our findings indicate that there is a cognitive bias favouring words representing large RWO over small ones but suggest several additional linguistic factors need to be controlled for it to be detected consistently in response time.

Exploratory factor analysis and validity of the virtual reality symptom questionnaire and computer use survey.

The widespread use of virtual reality head-mounted-displays (HMDs) calls for a re-examination of the impact of prolonged exposure to fixed visual displays at close ocular proximity. The purpose of this study is to validate the Virtual Reality Symptoms Questionnaire (VRSQ), created to understand symptoms of prolonged HMDs use, and Computer Use Survey (CUS), created to assess general physical and visual discomfort symptoms. Participants (N = 100) recorded their general discomfort symptoms using the CUS, performed an interactive task using a HMD for thirty minutes, and then answered the CUS again along with the VRSQ. VRSQ, analysed using an exploratory factor analysis, indicated a clear two-factor solution, and demonstrated very good internal consistency (α = 0.873). The CUS, also analysed using an exploratory factor analysis, indicated a four-factor solution, and demonstrated good internal consistency (α = 0.838). Practitioner Summary: A quantitative-experimental study was conducted to explore the factor structure and validate both the Virtual Reality Symptoms Questionnaire (VRSQ), and the Computer Use Survey (CUS). Findings indicate the VRSQ and CUS are precise and accurate survey instruments for evaluating discomfort after VR-HMD use and the latter for computer use. Abbreviations: VRSQ: virtual reality symptom questionnaire; CUS: computer use survey; OLED: organic light-emitting diode; MSQ: pensacola motion symptom questionnaire; SSQ: simulator sickness questionnaire; 3 D: three-dimensional computer generated space; VR: virtual reality; VR-HMD: virtual reality head-mounted-display; HMDs: head-mounted-displays; EFA: exploratory factor analysis.