It is well documented that exergames are enjoyable to play and can significantly improve older adults’ health and well-being. However, there is limited research on exploring factors affecting these users’ acceptance of such games, especially in virtual reality (VR), a relatively newer technology. This study proposes an extended version of the Technology Acceptance Model (TAM). We use variables from TAM related to older Chinese adults and specific to VR exergames to explore and confirm critical factors that could influence these users’ acceptance of such games in VR. We tested the proposed model with 51 older Chinese adults (aged 65 and above) after playing three commercial VR exergames (Beat Saber, FitXR, Dance Central). Results show that these older adults who are younger and retired and have a higher education, better financial means, and a good health condition have a more positive view of VR exergames. In addition, Perceived Usefulness, Perceived Ease of Use, and Perceived Enjoyment positively affect the intention to play VR exergames. Self-Satisfaction has a positive impact on Perceived Ease of Use and Perceived Usefulness. However, unlike previous studies, our results suggest that Facilitating Conditions have a negative effect on Perceived Ease of Use. Finally, we discuss the theoretical and practical implications of our results.

Cross-modality visible-infrared person reidentification (VI-ReID), which aims to retrieve pedestrian images captured by both visible and infrared cameras, is a challenging but essential task for smart surveillance systems. The huge barrier between visible and infrared images has led to the large cross-modality discrepancy and intraclass variations. Most existing VI-ReID methods tend to learn discriminative modality-sharable features based on either global or part-based representations, lacking effective optimization objectives. In this article, we propose a novel global-local multichannel (GLMC) network for VI-ReID, which can learn multigranularity representations based on both global and local features. The coarse- and fine-grained information can complement each other to form a more discriminative feature descriptor. Besides, we also propose a novel center loss function that aims to simultaneously improve the intraclass cross-modality similarity and enlarge the interclass discrepancy to explicitly handle the cross-modality discrepancy issue and avoid the model fluctuating problem. Experimental results on two public datasets have demonstrated the superiority of the proposed method compared with state-of-the-art approaches in terms of effectiveness.

We proposed a Lawson-norm-based adaptive estimation algorithm within the affine-project theory framework and give a name of Lawson-norm adaptive filter (LNAF) algorithm. The LNAF algorithm is derived, analyzed, and simulated for echo cancellation when background noise is impulsive, which is realized and implemented via using Lawson norm of past errors to take a sliding window on cost function to speed up convergence and achieve robustness for impulsive noises for colored signal. Simulations based on measured data are used for in-car echo cancellation and channel estimation to verify the LNAF algorithm’s performance with different inputs, which prove that the LNAF algorithm is superior to the popular AP algorithms under different impulsive interferences.

This brief proposed a constrained $q$ -Rényi kernel functioned (CqRKF) adaptive algorithm, which used new cost function created by $q$ -Rényi kernel function within the solution of constrained adaptive algorithms. The proposed CqRKF is implemented via solving a constrained optimization problem to provide superior performance against non-Gaussian noises. The theoretical analysis of steady-state mean square deviation (MSD) of proposed CqRKF has been presented and investigated under Gaussian and non-Gaussian noises. Computer simulations give a confirmation of the good match between the theoretical and the simulated values. For the sake of comparison, we carried out several computer simulations for beamforming and channel estimation, and simulation results shown that the CqRKF provides superior performance than the other conventional methods in non-Gaussian environment.

This study quantitatively investigated optimal finger-based entry size in touch-based mobile phones for two commonly used Chinese handwriting input styles: two-handed entry with the nondominant hand holding the device and the index finger of the dominant hand entering characters, and one-handed entry with the dominant hand holding the device and the thumb of the dominant hand being used for character entry. Results were assessed in terms of the number and length of protruding strokes, writing time, stroke writing speed, size ratio, number of writing attempts, and subjective preference. For both one-handed entry and two-handed entry, the optimal entry box size was found to be 2.5 × 2.5 cm. This size entry box is large enough for fast and accurate handwriting with high-entry-area utilization rate and few, short protruding strokes. The experimental results and methodology of this study can be employed in user interface design for handwriting in touch-based mobile phones.

Body-centric locomotion allows users to control both movement speed and direction with body parts (e.g., head tilt, arm swing or torso lean) to navigate in virtual reality (VR). However, there is little research to systematically investigate the effects of body parts for speed and direction control on virtual locomotion by taking in account different transfer functions(L: linear function, P: power function, and CL: piecewise function with constant and linear function). Therefore, we conducted an experiment to evaluate the combinational effects of the three factors (body parts for direction control, body parts for speed control, and transfer functions) on virtual locomotion. Results showed that (1) the head outperformed the torso for movement direction control in task completion time and environmental collisions; (2) Arm-based speed control led to shorter traveled distances than both head and knee. Head-based speed control had fewer environmental collisions than knee; (3) Body-centric locomotion with CL function was faster but less accurate than both L and P functions. Task time significantly decreased from P, L to CL functions, while traveled distance and overshoot significantly increased from P, L to CL functions. L function was rated with the highest score of USE-S , -pragmatic and -hedonic ; (4) Transfer function had a significant main effect on motion sickness: the participants felt more headache and nausea when performing locomotion with CL function. Our results provide implications for body-centric locomotion design in VR applications.