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Car-lock sounds are designed to inform the lock status of vehicles. However, drivers often experience a lack of confidence regarding whether the car is locked, and car thefts persistently occur, frequently attributed to unlocked doors. Without identification of critical factors for evaluating effects of car-lock sounds on drivers, a strategy to car-lock sound design with increased locking efficiency remains implicit. This study proposes a method to identify critical factors influencing drivers' perceived certainty of car-lock status and behaviours during car-locking. An experiment was conducted to simulate the locking process and verbal protocol analysis was employed to comprehend participants' cognitive processes and behaviours. The results show that mechanical sound yielded high certainty and few hesitations, while tonal and crisp sound elicited low certainty and frequent hesitations. Seven critical factors on participants' behaviours and cognitive processes were identified, which provides a data-driven approach for future research in car-lock sounds evaluation and design.

The effect of car-lock sounds on drivers is significant to inform the locking status of vehicles. However, the strategy for car-lock sounds evaluation remains implicit. This study proposes a method to identify critical factors on drivers' behaviours and cognitive processes that would inform further car-lock sounds evaluation and design.

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BACKGROUND: Implicit magnitudes and distribution of excessive contact pressures under hand orthoses hinder clinicians from precisely adjusting them to relieve the pressures. To address this, contact pressure under a hand orthosis were analysed using finite element method. METHODS: This paper proposed a method to numerically predict the relatively high magnitudes and critical distribution of contact pressures under hand orthosis through finite element analysis, to identify excessive contact pressure locations. The finite element model was established consisting of the hand, orthosis and bones. The hand and bones were assumed to be homogeneous and elastic bodies, and the orthosis was considered as an isotropic and elastic shell. Two predictions were conducted by assigning either low (fat) or high (skin) material stiffness to the hand model to attain the range of pressure magnitudes. An experiment was conducted to measure contact pressures at the predicted pressure locations. RESULTS: Identical pressure distributions were obtained from both predictions with relatively high pressure values disseminated at 12 anatomical locations. The highest magnitude was found at the thumb metacarpophalangeal joint with the maximum pressure range from 13 to 78 KPa. The measured values were within the predicted range of pressure magnitudes. Moreover, 6 excessive contact pressure locations were identified. CONCLUSIONS: The proposed method was verified by the measurement results. It renders understanding of interface conditions underneath the orthosis to inform clinicians regarding orthosis design and adjustment. It could also guide the development of 3D printed or sensorised orthosis by indicating optimal locations for perforations or pressure sensors.

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Customized static orthoses in rehabilitation clinics often cause side effects, such as discomfort and skin damage due to excessive local contact pressure. Currently, clinicians adjust orthoses to reduce high contact pressure based on subjective feedback from patients. However, the adjustment is inefficient and prone to variability due to the unknown contact pressure distribution as well as differences in discomfort due to pressure across patients. This paper proposed a new method to predict a threshold of contact pressure (pressure limit) associated with moderate discomfort at each critical spot under hand orthoses. A new pressure sensor skin with 13 sensing units was configured from FEA results of pressure distribution simulated with hand geometry data of six healthy participants. It was used to measure contact pressure under two types of customized orthoses for 40 patients with bone fractures. Their subjective perception of discomfort was also measured using a 6 scores discomfort scale. Based on these data, five critical spots were identified that correspond to high discomfort scores (>1) or high pressure magnitudes (>0.024 MPa). An artificial neural network was trained to predict contact pressure at each critical spot with orthosis type, gender, height, weight, discomfort scores and pressure measurements as input variables. The neural networks show satisfactory prediction accuracy with R2 values over 0.81 of regression between network outputs and measurements. This new method predicts a set of pressure limits at critical locations under the orthosis that the clinicians can use to make orthosis adjustment decisions.

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The delayed delivery, poor fitting and discomfort of customised orthoses are reported in rehabilitation clinics as resulting in more invasive interventions. The current practice of orthosis customisation relies heavily upon the experience and fabrication processes of therapists. In order to better understand the current practice, and thus identify data that is required for better comfort moving towards a data-driven customisation, this article describes a study generating working models of therapists. Customisations of hand and wrist orthoses for 18 patients were observed. Verbal protocol analysis was employed to extend the current understanding of fabrication processes. Working models of four therapists were established with quantitative evaluation on major phases, interactive activities and iterations of performing tasks during fabrication, revealing different working models between in- and out-patient departments (e.g. fabrication for in-patients was more complex and focussed on ergonomic fitting whereas fabrication for out-patients paid attention to durability) which were qualitatively explained. Practitioner summary: Fit and comfort are imperative for orthosis design and fabrication, however the current practice of customisation of an orthosis relies upon the experience of individual hand therapist. The article presents working models of hand therapists, and relevant data that would enable customisation of orthosis for better fit. Abbreviations: VPA: verbal protocol analysis; h&w: hand and wrist; LTT: low temperature thermoplastic; ANOVA: analysis of variance.

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BACKGROUND: Past research on anthropometry, especially in the industry of external ear worn products, stresses that positive comfort is enhanced when there is sufficient knowledge of human factors; however, most anthropometric studies focus only on the acquirement and presentation of data. OBJECTIVE: The aim of this paper is to provide with different methods to support design applications of 3-dimensional head and ear data with a focus on external ear products. METHODS: Two hundred persons representing the Danish population were scanned. The 3D data was collected, refined and analysed in 3 meaningful ways: Advanced geometry, visualisations of data and for the generation of archetypes. RESULTS: A matrix containing 29 new ear dimensions was generated. The application of methods led to the development of 9 additional dimensions. The paper finally presents all phases of the analysis of the 3D data in the form of a methodological framework. CONCLUSIONS: The paper contributes with, in addition to the methodological framework, techniques to extract data based on product understanding and how the data can be used to define archetypes for focus groups and other qualitative assessments. In their endeavour to develop successful and comfortable products designers should focus more on fitting the task into the human by benchmarking human dimensions against product data.

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