Wearables are used to recognize human activities in various applications. However, there is limited evidence on the comfort feelings in using wearables, which is crucial for the adoption and long-term engagement of users in those applications. In this paper, we propose the concept of comfort wearables in the context of in-flight posture recognition. A comfort wearable and a tight-fit version, using identical hardware and software architecture, were prototyped and tested by 35 participants in a Boeing 737 cabin. During the usage of each wearable, participants were asked to perform seven frequently observed in-flight sitting postures and report their overall comfort/discomfort afterwards. A multilayer perceptron neural network was used to classify those activities. Experiment results indicated that participants appreciated the comfort wearable, rating it with significantly higher comfort scores and lower discomfort scores. Cross-validation results also revealed that using the comfort wearable achieved even better accuracy (74.8%) than using the tight-fit wearable (65.8%) in posture recognition. Outcomes of the study demonstrate that ergonomic design and technical accuracy are not competing factors in the wearable design and highlight the opportunities for designing and using comfort wearables in broader contexts.@en

Hand anthropometry is one of the fundamentals of ergonomic research and product design. Many studies have been conducted to analyze the hand dimensions among different populations, however, the definitions and the numbers of those dimensions were usually selected based on the experience of the researchers and the available equipment. Few studies explored the importance of each hand dimension regarding the 3D shape of the hand. In this paper, we aim to identify the dominant dimensions that influence the hand shape variability while considering the stability of the measurements in practice. A novel four-step research method was proposed where in the first step, based on literature study, we defined 58 landmarks and 53 dimensions for the exploration. In the second step, 80,000 virtual hand models, each had the associated 53 dimensions, were augmented by changing the weights of Principle Components (PCs) of a statistical shape model (SSM). Deep neural networks (DNNs) were used to establish the inverse relationships from the dimensions to the weight of each PC of the hand SSM. Using the structured sparsity learning method, we identified 21 dominant dimensions that represent 90% of the variance of the hand shape. In the third step, two different manual measuring methods were used to evaluate the stability of the measurements in practice. Finally, we selected 16 dominant dimensions with lower measurement variance by synthesizing the findings in Step 2 and 3. It was concluded that the recognized 21 dominant dimensions can be treated as the reference dimensions for anthropometric study and using the selected 16 dominant dimensions with lower measurement variance, ergonomists are able to generate a 3D hand model based on simple measurement tools with an accuracy of 5.9 mm. Though the accuracy is limited, the efforts are minimum, and the results can be used as an indicator in the early stage of research/design.

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Acquiring an accurate 3D scan of the human hand is a challenging task, mainly due to the complicated geometry and the instability of the hand. In this paper, we present a low-cost photogrammetry-based scanner that is designed for scanning the human hand. The scanner has fifty modules, each has a Raspberry Pi with an 8-megapixels camera. They are uniformly positioned in two parallel frames and 96% of a hand surface can be viewed by at least 3 cameras. Using the timestamp method, we synchronize the shutters of the 50 cameras within the range of 80 ms to minimize the influence of the instability of the hand. Moreover, the scanner is easy to build with its modular design, and easy to operate with a laptop that is connected to the system by WiFi. Using a 3D printed prosthetic hand, we compared the 3D scanning accuracy of the proposed scanner with the Artec Spider® scanner. The mean absolute error between the two scans is 0.62 ± 0.28 mm. It is concluded that the proposed hand scanner can be used as a low-cost yet accurate tool in many applications, such as personalized product design.

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A high-fidelity digital representation of (part of) the human body is a key enabler for integrating humans in a digital twin. Among different parts of human body, building the model of the hand can be a challenging task due to the posture deviations among collected scans. In this article, we proposed a posture invariant statistical shape model (SSM) of the human hand based on 59 3D scans of human hands. First, the 3D scans were spatially aligned using a Möbius sphere-based algorithm. An articulated skeleton, which contains 20 bone segments and 16 joints, was embedded for each 3D scan. Then, all scans were aligned to the same posture using the skeleton and the linear blend skinning (LBS) algorithm. Three methods, i.e., principal component analysis (PCA), kernel-PCA (KPCA) with different kernel functions, and independent component analysis (ICA), were evaluated in the construction of the SSMs regarding the compactness, the generalization ability, and the specificity. The PCA-based SSM was selected, where 20 principal components were used as parameters for the model. Results of the leave-one-out validation indicate that the proposed model was able to fit a given 3D scan of the human hand at an accuracy of 1.21 ± 0.14 mm. Experiment results also indicated that the proposed SSM outperforms the SSM that was built on the scans without posture correction. It is concluded that the proposed posture correction approach can effectively improve the accuracy of the hand SSM and therefore enables its wide usage in human-integrated digital twin applications.

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Fast, accurate and low-cost 3D scans are the key in designing personalized products. In this paper, using close-range photogrammetry technique, we aim at finding the “just enough” number of cameras and their spatial configurations for a full 3D reconstruction of the human hand. Given an object, we establish a mathematical model to describe the 3D constructible ratio based on the field of the view and the depth of field of each camera, as well as the visibility of each part of the object in the view of each camera. Furthermore, we introduce spatial constrains to arrange cameras along two rings for: 1) solving the problem of the large number of parameters in the unconstrained optimization, and 2) the feasibility and flexibility in the construction. Based on the found number of cameras and the spatial configuration of each camera, a prototype scanner was built to verify the effectiveness of the proposed method. The mean absolute error between the 3D scan of a 3D printed hand and its original CAD model was found to be 0.38mm, which is smaller than that (0.52mm) of using the conventional setup. Besides, the distribution of errors is smaller as well, which implicates a better full 3D reconstruction of the scanned hand.@en