What Aimed Movement Models Fit Distal Pointing With Varying Depth?

OBJECTIVE: With the rapid improvements in drone technology, there is an increasing interest in distal pointing to diffuse drones. This study investigated the effect of depth on distal pointing when the hand does not traverse the entire distance from start to target so that the most suitable mathematical model can be assessed. BACKGROUND: Starting from the Fitts paradigm, researchers have proposed different models to predict movement time when the distance to the target is variable. They do consider distance, but they are based on statistical modeling rather than the underlying control mechanisms. METHODS: Twenty-four participants volunteered for an experiment in a full-factorial Fitts' paradigm task (3 levels of movement amplitude *7 levels of target width *3 levels of distance from participant to screen). Movement time and the number of errors were the dependent variables. RESULTS: Depth has a significant effect when the target width is small, but depth has no effect when the target width is large. The angular version of the two-part model is superior to the one-part Fitts' model at larger distances. Besides, Index of difficulty for distal pointing, IDDP with adjustable k achieves the best fit even though the model is very sensitive to the value of k and the complexity of the model could be resulting in an overfitting. The result implies that the effects of movement amplitude and target width are not comparable and grouping them to form a dependent index of difficulty can be misleading especially when distance is an added variable. CONCLUSION: The angular version of the two-part model is a viable and meaningful description for distal pointing. Even though the IDDP with adjustable k is the best predictor for movement time when depth is an added variable, there is no physical interpretation for it. APPLICATION: A reasonable predictive model for performance assessments and predictions in distal pointing.

An alternative tool for triaging patients with possible acute coronary symptoms before admission to a chest pain unit.

OBJECTIVE: This study aimed to develop a triage tool to more effectively triage possible ACS patients presenting to the emergency department (ED) before admission to a protocol-driven chest pain unit (CPU). METHODS: Seven hundred ninety-three clinical cases, randomly selected from 7962 possible ACS cases, were used to develop and test an ACS triage model using cluster analysis and stepwise logistic regression. RESULTS: The ACS triage model, logit (suspected ACS patient)=-5.283+1.894×chest pain+1.612×age+1.222×male+0.958×proximal radiation pain+0.962×shock+0.519×acute heart failure, with a threshold value set at 2.5, was developed to triage patients. Compared to four existing methods, the chest-pain strategy, the Zarich's strategy, the flowchart, and the heart broken index (HBI), the ACS triage model had better performance. CONCLUSION: This study developed an ACS triage model for triaging possible ACS patients. The model could be used as a rapid tool in EDs to reduce the workloads of ED nurses and physicians in relation to admissions to the CPU.

Graphical message transmission using the monotonic vibration function of a smart phone.

This study aimed at evaluating four vibrotactile capabilities for perceiving graphical information presented on a smart phone. Thirty-two blindfolded college students participated in four experiments to test their capabilities of two-point discrimination, relative and absolute judgments of line thickness, and recognition of basic shapes. All the information was received through the default vibration function of the phone, sensed by their scanning fingers. The results showed a good two-point discrimination accuracy rate, reaching 98.8% when the distance between two points was set at 3.2 mm; the relative judgment of line thickness reached the level of 78.3% accuracy when the two-line width difference ratio was set at 3%; the absolute judgment reached the level of 78.8% when the participants recognised line thickness from one of two. Overall, especially for the shapes judgment, the information transmitted by the various codes may be quite low. These findings should inspire advanced investigations and provide design guidelines. PRACTITIONER SUMMARY: This study tested four vibrotactile capabilities for perception of graphical information when solely using the monotonic vibration function of a smart phone. The results show low information transmission. These findings encourage advanced investigations of new coding systems so that relevant mobile applications could be developed to help the visually impaired.

Measuring individual corrective reaction time using the intermittent illumination model.

The corrective reaction time (tcr) is an essential motor property when modelling hand control movements. Many studies designed experiments to estimate tcr, but reported only group means with inconsistent definitions. This study proposes an alternative methodology using Drury's (1994) intermittent illumination model. A total of 24 participants performed circular tracking movements under five levels of visual information delay using a modified monitor in a darkened room. Measured movement speeds and the manipulated delays were used with the model to estimate tcr of individuals and test effects of gender and path width. The results showed excellent model fits and demonstrated individual differences of tcr, which was 273 ms on average and ranged from 87 to 441 ms. The wide range of tcr values was due to significant effects of gender and path width. Male participants required shorter tcr compared to female participants, especially for narrow path widths. PRACTITIONER SUMMARY: This study reports the corrective reaction time (tcr) of individuals using a novel methodology. The estimated tcr ranged from 87 to 441 ms, helping model hand control movements, such as aiming and tracking. The methodology can be continuously applied to study tcr under conditions with various performers and movements.

Verification of models for ballistic movement time and endpoint variability.

A hand control movement is composed of several ballistic movements. The time required in performing a ballistic movement and its endpoint variability are two important properties in developing movement models. The purpose of this study was to test potential models for predicting these two properties. Twelve participants conducted ballistic movements of specific amplitudes using a drawing tablet. The measured data of movement time and endpoint variability were then used to verify the models. This study was successful with Hoffmann and Gan's movement time model (Hoffmann, 1981; Gan and Hoffmann 1988) predicting more than 90.7% data variance for 84 individual measurements. A new theoretically developed ballistic movement variability model, proved to be better than Howarth, Beggs, and Bowden's (1971) model, predicting on average 84.8% of stopping-variable error and 88.3% of aiming-variable errors. These two validated models will help build solid theoretical movement models and evaluate input devices. PRACTITIONER SUMMARY: This article provides better models for predicting end accuracy and movement time of ballistic movements that are desirable in rapid aiming tasks, such as keying in numbers on a smart phone. The models allow better design of aiming tasks, for example button sizes on mobile phones for different user populations.

Evaluating the Effectiveness of Complexity Features of Eye Movement on Computer Activities Detection.

Recently, tools developed for detecting human activities have been quite prominent in contributing to health issue prevention and long-term healthcare. For this occasion, the current study aimed to evaluate the performance of eye-movement complexity features (from multi-scale entropy analysis) compared to eye-movement conventional features (from basic statistical measurements) on detecting daily computer activities, comprising reading an English scientific paper, watching an English movie-trailer video, and typing English sentences. A total of 150 students participated in these computer activities. The participants' eye movements were captured using a desktop eye-tracker (GP3 HD Gazepoint™ Canada) while performing the experimental tasks. The collected eye-movement data were then processed to obtain 56 conventional and 550 complexity features of eye movement. A statistic test, analysis of variance (ANOVA), was performed to screen these features, which resulted in 45 conventional and 379 complexity features. These eye-movement features with four combinations were used to build 12 AI models using Support Vector Machine, Decision Tree, and Random Forest (RF). The comparisons of the models showed the superiority of complexity features (85.34% of accuracy) compared to conventional features (66.98% of accuracy). Furthermore, screening eye-movement features using ANOVA enhances 2.29% of recognition accuracy. This study proves the superiority of eye-movement complexity features.

Disclosing Critical Voice Features for Discriminating between Depression and Insomnia-A Preliminary Study for Developing a Quantitative Method.

Background: Depression and insomnia are highly related-insomnia is a common symptom among depression patients, and insomnia can result in depression. Although depression patients and insomnia patients should be treated with different approaches, the lack of practical biological markers makes it difficult to discriminate between depression and insomnia effectively. Purpose: This study aimed to disclose critical vocal features for discriminating between depression and insomnia. Methods: Four groups of patients, comprising six severe-depression patients, four moderate-depression patients, ten insomnia patients, and four patients with chronic pain disorder (CPD) participated in this preliminary study, which aimed to record their speaking voices. An open-source software, openSMILE, was applied to extract 384 voice features. Analysis of variance was used to analyze the effects of the four patient statuses on these voice features. Results: statistical analyses showed significant relationships between patient status and voice features. Patients with severe depression, moderate depression, insomnia, and CPD reacted differently to certain voice features. Critical voice features were reported based on these statistical relationships. Conclusions: This preliminary study shows the potential in developing discriminating models of depression and insomnia using voice features. Future studies should recruit an adequate number of patients to confirm these voice features and increase the number of data for developing a quantitative method.

Predicting Emotional Valence of People Living with the Human Immunodeficiency Virus Using Daily Voice Clips: A Preliminary Study.

To detect depression in people living with the human immunodeficiency virus (PLHIV), this preliminary study developed an artificial intelligence (AI) model aimed at discriminating the emotional valence of PLHIV. Sixteen PLHIV recruited from the Taoyuan General Hospital, Ministry of Health and Welfare, participated in this study from 2019 to 2020. A self-developed mobile application (app) was installed on sixteen participants' mobile phones and recorded their daily voice clips and emotional valence values. After data preprocessing of the collected voice clips was conducted, an open-source software, openSMILE, was applied to extract 384 voice features. These features were then tested with statistical methods to screen critical modeling features. Several decision-tree models were built based on various data combinations to test the effectiveness of feature selection methods. The developed model performed very well for individuals who reported an adequate amount of data with widely distributed valence values. The effectiveness of feature selection methods, limitations of collected data, and future research were discussed.