Assistive products for older adults in China: self-reported need, services, and satisfaction.

Purpose: Older adults may abandon or discontinue the use of assistive products due to low levels of satisfaction. Only few studies have examined need and satisfaction related to the use of assistive products for this group in China. As such, research is needed to improve satisfaction with assistive products and related services. Method: This study used technology acceptance theory to examine the self-reported need for, and ownership of, assistive products among older adults in China, as well as to examine the association between services and satisfaction with assistive products; the underlying mechanism of this association was also assessed. The current study used the rapid assistive technology assessment (rATA) questionnaire designed by the World Health Organization (WHO) for stratified and cluster sampling. A total of 2,158 older adults living in China were interviewed. The multiple regression analysis was used to examine the independent and interactive associations between services and satisfaction. Heterogeneity and robustness tests were also undertaken.Results: The self-reported need for assistive products pertained mainly to vision, and together with ownership, the need gap has not yet been addressed. Both pre-sale (ß = 0.600, p < 0.01) and follow-up services (ß = 0.270, p < 0.01) were positively correlated with satisfaction, which in turn shows heterogeneity when it comes to the types of assistive products.Conclusion: Providing accessible services, especially follow-up services, will help older adults be more satisfied with their assistive products, thus ultimately ensuring the continued use of products.

There is a huge demand for assistive products among older adults in China, but the awareness of these adults' needs is low.This study's findings confirm the results of earlier research, i.e. that satisfactory services are important for overall satisfaction with assistive products among older adults.Easy-to-access services may help to improve product satisfaction through service.Environmental factors are also important indicators of satisfaction with assistive products.

Spatially varying defocus map estimation from a single image based on spatial aliasing sampling method.

In current optical systems, defocus blur is inevitable due to the constrained depth of field. However, it is difficult to accurately identify the defocus amount at each pixel position as the point spread function changes spatially. In this paper, we introduce a histogram-invariant spatial aliasing sampling method for reconstructing all-in-focus images, which addresses the challenge of insufficient pixel-level annotated samples, and subsequently introduces a high-resolution network for estimating spatially varying defocus maps from a single image. The accuracy of the proposed method is evaluated on various synthetic and real data. The experimental results demonstrate that our proposed model outperforms state-of-the-art methods for defocus map estimation significantly.

Saliency enhancement method for photoacoustic molecular imaging based on Grüneisen relaxation nonlinear effect.

Photoacoustic molecular imaging technology has a wide range of applications in biomedical research. In practical scenarios, both the probes and blood generate signals, resulting in the saliency of the probes in the blood environment being diminished, impacting imaging quality. Although several methods have been proposed for saliency enhancement, they inevitably suffer from moderate generality and detection speed. The Grüneisen relaxation (GR) nonlinear effect offers an alternative for enhancing saliency and can improve generality and speed. In this article, the excitation and detection efficiencies are optimized to enhance the GR signal amplitude. Experimental studies show that the saliency of the probe is enhanced. Moreover, the issue of signal aliasing is studied to ensure the accuracy of enhancement results in the tissues. In a word, the feasibility of the GR-based imaging method in saliency enhancement is successfully demonstrated in the study, showing the superiorities of good generality and detection speed.

A three-stage deep learning-based training frame for spectra baseline correction.

For spectrometers, baseline drift seriously affects the measurement and quantitative analysis of spectral data. Deep learning has recently emerged as a powerful method for baseline correction. However, the dependence on vast amounts of paired data and the difficulty in obtaining spectral data limit the performance and development of deep learning-based methods. Therefore, we solve these problems from the network architecture and training framework. For the network architecture, a Learned Feature Fusion (LFF) module is designed to improve the performance of U-net, and a three-stage training frame is proposed to train this network. Specifically, the LFF module is designed to adaptively integrate features from different scales, greatly improving the performance of U-net. For the training frame, stage 1 uses airPLS to ameliorate the problem of vast amounts of paired data, stage 2 uses synthetic spectra to further ease reliance on real spectra, and stage 3 uses contrastive learning to reduce the gap between synthesized and real spectra. The experiments show that the proposed method is a powerful tool for baseline correction and possesses potential for application in spectral quantitative analysis.

Priors-assisted dehazing network with attention supervision and detail preservation.

Single image dehazing is a challenging computer vision task for other high-level applications, e.g., object detection, navigation, and positioning systems. Recently, most existing dehazing methods have followed a "black box" recovery paradigm that obtains the haze-free image from its corresponding hazy input by network learning. Unfortunately, these algorithms ignore the effective utilization of relevant image priors and non-uniform haze distribution problems, causing insufficient or excessive dehazing performance. In addition, they pay little attention to image detail preservation during the dehazing process, thus inevitably producing blurry results. To address the above problems, we propose a novel priors-assisted dehazing network (called PADNet), which fully explores relevant image priors from two new perspectives: attention supervision and detail preservation. For one thing, we leverage the dark channel prior to constrain the attention map generation that denotes the haze pixel position information, thereby better extracting non-uniform feature distributions from hazy images. For another, we find that the residual channel prior of the hazy images contains rich structural information, so it is natural to incorporate it into our dehazing architecture to preserve more structural detail information. Furthermore, since the attention map and dehazed image are simultaneously predicted during the convergence of our model, a self-paced semi-curriculum learning strategy is utilized to alleviate the learning ambiguity. Extensive quantitative and qualitative experiments on several benchmark datasets demonstrate that our PADNet can perform favorably against existing state-of-the-art methods. The code will be available at https://github.com/leandepk/PADNet.

Multi-Class Wound Classification via High and Low-Frequency Guidance Network.

Wound image classification is a crucial preprocessing step to many intelligent medical systems, e.g., online diagnosis and smart medical. Recently, Convolutional Neural Network (CNN) has been widely applied to the classification of wound images and obtained promising performance to some extent. Unfortunately, it is still challenging to classify multiple wound types due to the complexity and variety of wound images. Existing CNNs usually extract high- and low-frequency features at the same convolutional layer, which inevitably causes information loss and further affects the accuracy of classification. To this end, we propose a novel High and Low-frequency Guidance Network (HLG-Net) for multi-class wound classification. To be specific, HLG-Net contains two branches: High-Frequency Network (HF-Net) and Low-Frequency Network (LF-Net). We employ pre-trained models ResNet and Res2Net as the feature backbone of the HF-Net, which makes the network capture the high-frequency details and texture information of wound images. To extract much low-frequency information, we utilize a Multi-Stream Dilation Convolution Residual Block (MSDCRB) as the backbone of the LF-Net. Moreover, a fusion module is proposed to fully explore informative features at the end of these two separate feature extraction branches, and obtain the final classification result. Extensive experiments demonstrate that HLG-Net can achieve maximum accuracy of 98.00%, 92.11%, and 82.61% in two-class, three-class, and four-class wound image classifications, respectively, which outperforms the previous state-of-the-art methods.

Improved Thermoelectric Performance of Sb2Te3 Nanosheets by Coating Pt Particles in Wide Medium-Temperature Zone.

The p-type Sb2Te3 alloy, a binary compound belonging to the V2VI3-based materials, has been widely used as a commercial material in the room-temperature zone. However, its low thermoelectric performance hinders its application in the low-medium temperature range. In this study, we prepared Sb2Te3 nanosheets coated with nanometer-sized Pt particles using a combination of solvothermal and photo-reduction methods. Our findings demonstrate that despite the adverse effects on certain properties, the addition of Pt particles to Sb2Te3 significantly improves the thermoelectric properties, primarily due to the enhanced electronic conductivity. The optimal ZT value reached 1.67 at 573 K for Sb2Te3 coated with 0.2 wt% Pt particles, and it remained above 1.0 within the temperature range of 333-573 K. These values represent a 47% and 49% increase, respectively, compared to the pure Sb2Te3 matrix. This enhancement in thermoelectric performance can be attributed to the presence of Pt metal particles, which effectively enhance carrier and phonon transport properties. Additionally, we conducted a Density Functional Theory (DFT) study to gain further insights into the underlying mechanisms. The results revealed that Sb2Te3 doped with Pt exhibited a doping level in the band structure, and a sharp rise in the Density of States (DOS) was observed. This sharp rise can be attributed to the presence of Pt atoms, which lead to enhanced electronic conductivity. In conclusion, our findings demonstrate that the incorporation of nanometer-sized Pt particles effectively improves the carrier and phonon transport properties of the Sb2Te3 alloy. This makes it a promising candidate for medium-temperature thermoelectric applications, as evidenced by the significant enhancement in thermoelectric performance achieved in this study.

Skylight Polarization Pattern Simulator Based on a Virtual-Real-Fusion Framework for Urban Bionic Polarization Navigation.

In a data-driven context, bionic polarization navigation requires a mass of skylight polarization pattern data with diversity, complete ground truth, and scene information. However, acquiring such data in urban environments, where bionic polarization navigation is widely utilized, remains challenging. In this paper, we proposed a virtual-real-fusion framework of the skylight polarization pattern simulator and provided a data preparation method complementing the existing pure simulation or measurement method. The framework consists of a virtual part simulating the ground truth of skylight polarization pattern, a real part measuring scene information, and a fusion part fusing information of the first two parts according to the imaging projection relationship. To illustrate the framework, we constructed a simulator instance adapted to the urban environment and clear weather and verified it in 174 urban scenes. The results showed that the simulator can provide a mass of diverse urban skylight polarization pattern data with scene information and complete ground truth based on a few practical measurements. Moreover, we released a dataset based on the results and opened our code to facilitate researchers preparing and adapting their datasets to their research targets.

Prediction of Impulsive Aggression Based on Video Images.

In response to the subjectivity, low accuracy, and high concealment of existing attack behavior prediction methods, a video-based impulsive aggression prediction method that integrates physiological parameters and facial expression information is proposed. This method uses imaging equipment to capture video and facial expression information containing the subject's face and uses imaging photoplethysmography (IPPG) technology to obtain the subject's heart rate variability parameters. Meanwhile, the ResNet-34 expression recognition model was constructed to obtain the subject's facial expression information. Based on the random forest classification model, the physiological parameters and facial expression information obtained are used to predict individual impulsive aggression. Finally, an impulsive aggression induction experiment was designed to verify the method. The experimental results show that the accuracy of this method for predicting the presence or absence of impulsive aggression was 89.39%. This method proves the feasibility of applying physiological parameters and facial expression information to predict impulsive aggression. This article has important theoretical and practical value for exploring new impulsive aggression prediction methods. It also has significance in safety monitoring in special and public places such as prisons and rehabilitation centers.

Single-wavelength sO2 measurement based on Grüneisen-relaxation photoacoustic effect.

The photoacoustic effect-based sO2 measurement is attracting more and more attention due to its non-invasiveness and accuracy. Compared with the linear dual-wavelength method, the sO2 measurement based on single-wavelength excitation can be potentially applied with simplified system construction. However, the single-wavelength methods proposed in previous studies decreases the safety or lacks the in-depth resolution. This paper proposes a novel single-wavelength method based on the Grüneisen-relaxation (GR) nonlinear effects. It avoids the high fluence excitation with maintaining in-depth resolution and obtains the signals in hundreds of nanoseconds, simultaneously improving the safety and detection speed. The construction of a single laser source for GR effect generation makes the system stable. The sO2 quantification results of blood samples have a good consistency with the reference values. Our work provides a safer and faster measurement method, and a stable system, to promote its application in the clinical area.

Window filtering algorithm for a low repetition rate pulsed laser coherent combination system.

The multi-dithering method has been well verified in the phase-locking of polarization coherent combination experiments. However, it is difficult to apply to low repetition rate pulsed laser coherent combination, since there exists an overlap in the frequency domain between the pulse laser and the large amplitude-phase noise resulting in traditional filters being unable to effectively separate the phase noise. Aiming to solve the problem, we propose, to the best of our knowledge, a novel method of pulse noise detection, identification, and filtering based on the autocorrelation characteristics between noise signals. The self-designed adaptive window filtering algorithm can effectively filter the pulse signal doped in the phase noise around 0.1 ms. After the pulses are filtered out, the remaining phase noise signal is used as the input signal of the multi-dithering method for phase locking; the phase difference of two pulsed beams (10 kHz) is successfully compensated to zero; and the coherent combination of the closed-loop phase lock is realized. Simultaneously, the phase correction periods are short, the phase lock effect is stable, and the intensity of the final combined pulses rises to the ideal value (0.9Imax). In addition, the adaptive window filtering algorithm we proposed can be applied to the coherent combined system of large array fiber lasers and further lay the foundation for fiber phased array lidar.

Ultralow loss hollow-core negative curvature fibers with nested elliptical antiresonance tubes.

Hollow-core negative curvature fibers can confine light within air core and have small nonlinearity and dispersion and high damage threshold, thereby attracting a great deal of interest in the field of hollow core fibers. However, reducing the loss of hollow-core negative curvature fibers is a serious problem. On this basis, three new types of fibers with different nested tube structures are proposed in the near-infrared spectral regions and compared in detail with a previously proposed hollow-core negative curvature fiber. We used finite-element method for numerical simulation studies of their transmission loss, bending loss, and single-mode performance, and then the transmission performance of various structural fibers is compared. We found that the nested elliptical antiresonant fiber 1 has better transmission performance than that of the three other types of fibers in the spectral range of 0.72-1.6 µm. Results show that the confinement loss of the LP01 mode is as low as 6.45×10-6 dB/km at λ = 1.06 µm. To the best of our knowledge, the record low level of confinement loss of hollow-core antiresonant fibers with nested tube structures was created. In addition, the nested elliptical antiresonant fiber 1 has better bending resistance, and its bending loss was below 2.99×10-2 dB/km at 5 cm bending radius.

Rational selection of RGB channels for disease classification based on IPPG technology.

The green channel is usually selected as the optimal channel for vital signs monitoring in image photoplethysmography (IPPG) technology. However, some controversies arising from the different penetrability of skin tissue in visible light remain unresolved, i.e., making the optical and physiological information carried by the IPPG signals of the RGB channels inconsistent. This study clarifies that the optimal channels for different diseases are different when IPPG technology is used for disease classification. We further verified this conclusion in the classification model of heart disease and diabetes mellitus based on the random forest classification algorithm. The experimental results indicate that the green channel has a considerably excellent performance in classifying heart disease patients and the healthy with an average Accuracy value of 88.43% and an average F1score value of 93.72%. The optimal channel for classifying diabetes mellitus patients and the healthy is the red channel with an average Accuracy value of 82.12% and the average F1score value of 89.31%. Due to the limited penetration depth of the blue channel into the skin tissue, the blue channel is not as effective as the green and red channels as a disease classification channel. This investigation is of great significance to the development of IPPG technology and its application in disease classification.

Nondestructive testing and 3D imaging of PE pipes using terahertz frequency-modulated continuous wave.

Polyethylene (PE) pipes are widely used as the main carrier for the transportation of natural gas, so nondestructive testing techniques for PE pipes are essential for the safety of natural gas transportation. In order to compensate for the shortcomings of conventional inspection methods, a terahertz (THz) three-dimensional imaging system for nondestructive inspection of PE pipes is designed. The system is based on frequency-modulated continuous-wave (FMCW) technology, with a THz source bandwidth of 0.225-0.330 THz and an output power of over 5 mW, which can achieve submillimeter spatial resolution in three dimensions. The system is used to scan PE pipes in three dimensions in a laboratory environment, and the results show that the system could achieve nondestructive testing and three-dimensional imaging of different defects in PE pipes. In addition, combined with the deep-learning-based THz transformer network, the intelligent identification of different defects is realized, and the accuracy rate can reach up to 88%. The above results provide technical guidance for the application of THz FMCW systems in the actual detection of PE pipes, and provide supplements and improvements for traditional detection methods.

Optimized Golay-9 array configurations for mid-frequency compensation in optical sparse aperture systems.

Optical sparse aperture (OSA) imaging systems show great potential to generate higher resolution images than those of equivalent single filled aperture systems. However, due to the sparsity and dispersion of sparse aperture arrays, pupil function is no longer a connected domain, which further attenuates or loses the mid-frequency modulation transfer function (MTF), resulting in lower mid-frequency contrast and blurred images. Therefore, an improved traversal algorithm is proposed to optimize Golay-9 array configurations for compensating the mid-frequency MTF. Its structural parameters include diameters of sub-apertures, relative rotation angles between individual sub-apertures, and radius of concentric circles. Then, these parameters are traversed successively in order. Finally, the influences of the obtained optimized array configurations on the mid-frequency MTF are analyzed in detail, and the image performances are evaluated. The experimental results prove the contrast enhancement. Compared with a Golay-9 array at F=36.5%, the maximum MTF increases from 0.1503 to 0.307, and the mid-frequency MTF is boosted from 0.0565 to 0.0767. In addition, the peak signal to noise ratio of the degraded image is promoted from 19.75 dB to 20.63 dB. Both quantitative and qualitative evaluations demonstrate the validity of the proposed method.

MTF improvement for optical synthetic aperture system via mid-frequency compensation.

Optical synthetic aperture imaging system has grown out the quest for higher angular resolution in astronomy, which combines the radiation from several small sub-apertures to obtain a resolution equivalent to that of a single filled aperture. Due to the discrete distribution of the sub-apertures, pupil function is no longer a connected domain, which further leads to the attenuation or loss of the mid-frequency modulation transfer function (MTF). The mid-frequency MTF compensation is therefore a key focus. In this paper, a complete mid-frequency compensation algorithm is proposed, which can extract and fuse the frequency of different synthetic aperture systems and monolithic aperture systems according to their special MTF characteristics. The dimensions of the monolithic aperture and optical synthetic aperture system are derived, and the longest baseline of the monolithic aperture is much smaller than that of the optical synthetic aperture system. Then the separated spatial frequency information is extracted and synthesized according to the spatial frequency equivalence point. Finally, the full-frequency enhanced image is recovered by using improved Wiener-Helstrom filter, which adopts specific parameters based on different sub-aperture arrangements. The mid-frequency MTF of Golay-3 increases from 0.12 to 0.16 and that of Golay-6 increases from 0.06 to 0.18. Both the simulation and experiment prove that the proposed method not only realizes the spatial resolution determined by the longest baseline of the optical synthetic aperture system, but also successfully compensates its mid-frequency MTF.

Breadth-first piston diagnosing approach for segmented mirrors through supervised learning of multiple-wavelength images.

Piston diagnosing approaches for segmented mirrors via machine-learning have shown great success. However, they are inevitably challenged with 2π ambiguity, and the accuracy is usually influenced by the location and number of submirrors. A piston diagnosing approach for segmented mirrors, which employs the breadth-first search (BFS) algorithm and supervised learning strategies of multi-wavelength images, is investigated. An original kind of object-independent and normalized dataset is generated by the in-focal and defocused images at different wavelengths. Additionally, the segmented mirrors are divided into several sub-models of binary tree and are traversed through the BFS algorithm. Furthermore, two deep image-based convolutional neural networks are constructed for predicting the ranges and values of piston aberrations. Finally, simulations are performed, and the accuracy is independent of the location and number of submirrors. The Pearson correlation coefficients for test sets are above 0.99, and the average root mean square error of segmented mirrors is approximately 0.01λ. This technique allows the piston error between segmented mirrors to be measured without 2π ambiguity. Moreover, it can be used for data collected by a real setup. Furthermore, it can be applied to segmented mirrors with different numbers of submirrors based on the sub-model of a binary tree.

Enhancement of terahertz waves from two-color laser-field induced air plasma excited using a third-color femtosecond laser.

This study experimentally demonstrates and theoretically analyzes the enhancement of terahertz (THz) waves from two-color laser-field (consisting of a near-infrared femtosecond laser and its second-harmonic wave) induced air plasma using an additional 800 nm femtosecond laser. The experiments revealed that the additional 800 nm laser increased the THz energy up to 22 times. To understand the enhancement mechanism and reveal the maximum enhancement conditions, the effects of the 800 nm beam's polarization and energy variations of both beams on the THz amplification were studied. With the increase in the 800 nm pulse energy, the THz yield initially increases, and then decreases after reaching an inflection point. The THz increase rate continues to increase with the decrease in energy of the near-infrared two-color fields. The 800 nm beam could efficiently modulate the THz spectral energy distribution by increasing the high-frequency components, while decreasing the low-frequency components.

Application of neural network model in assisting device fitting for low vision patients.

BACKGROUND: To explore the application of neural network models in artificial intelligence (AI)-aided devices fitting for low vision patients. METHODS: The data of 836 visually impaired people were collected in southwestern Fujian from May 2014 to May 2017. After a full eye examination, 629 low vision patients were selected from this group. Based on the visual functions, rehabilitation needs, and living quality scores of the selected patients, the professionals chose assistive devices that were the best fit for the patients. The data of these three factors were then subjected to the quantitative analysis, and the results were digitized and labeled. The final datasets were used to train a fully connected deep neural networks to obtain an AI-aided model for assistive device fitting. RESULTS: In this study, the main causes of low vision in southwestern Fujian were congenital diseases, among which congenital cataract was the most common. During the low vision AI-aided devices fitting, we found that the intermediate distance magnifier was suitable for the largest number of patients. Through quantitative analysis of the research results, it was found that AI-aided devices fitting was closely related to visual function, rehabilitation needs and quality of life. If this complex relationship can be mapped into the neural network model, AI-aided device fitting can be realized. We built a fully connected neural network model for AI-aided device fitting. The input of the model was the characteristic data of low vision patients, and the output was the forecast of suitable devices. When the threshold of the model was 0.4, the accuracy was about 80% and the F1 value was about 0.31. This threshold can be used as the classification judgment threshold of the model. CONCLUSIONS: Low vision AI-aided device fitting is closely related to visual function, rehabilitation needs, and quality of life scores. The neural network model based on full connection can achieve high accuracy in AI-aided devices fitting. It has a great impact on clinical application.

A camera-based ballistocardiogram heart rate measurement method.

Recent studies have shown that head movements associated with cardiac activity contain a heart rate (HR) signal. In most previous studies, subjects were required to remain stationary in a specific environment during HR measurements, and measurement accuracy depended on the choice of target in the scene, i.e., the specified region of the face. In this paper, we proposed a robust HR measurement method based on ballistocardiogram (BCG) technology. This method requires only a camera and does not require that users establish a complex measurement environment. In addition, a bidirectional optical flow algorithm is designed to select and track valid feature points in the video captured by using the camera. Experiments with 11 subjects show that the HR values measured using the proposed method differ slightly from the reference values, and the average error is only 1.09%. Overall, this method can improve the accuracy of BCG without limitations related to skin tone, illumination, the state of the subject, or the test location.