Not only has information technology evolved rapidly, but the spatial cognition theory for blind and visually impaired (BVI) people has also made great strides, which has opened up a new opportunity for indoor travel assistance systems (ITASs). However, there are still some issues that have not been effectively addressed due to the lack of guidance of the spatial cognition theory. Thus, this article presents a comparative survey among ITASs proposed in the last four years in an effort to inform researchers and developers about system problems and challenges and inform BVI people about the various types and functions of the ITAS. This article will also make researchers and developers aware of the importance of the spatial cognition theory. Furthermore, we give predictions for future trends based on a detailed analysis of 17 ITASs.

Modern inertial measurement units (IMUs) with low cost, small size, and low power consumption are the key to improving indoor positioning accuracy. However, the performance of IMU in current mobile phones is spotty. The IMU positioning error of mainstream cell phones is basically greater than 1%, which makes it difficult for indoor fusion positioning technology to achieve accuracy within 3 m on a mobile phone, seriously restricting the development of the industry. Therefore, the existing indoor fusion algorithms need to introduce additional hardware, such as high-density UWB/Wi-Fi/Bluetooth deployment, to compensate for the lack of IMU performance, which significantly increases the deployment cost, difficulty, and reduces environmental applicability. It is found that deep learning can greatly improve the performance of IMU, but traditional deep learning methods have problems such as privacy protection, difficulty in data collection, and low training efficiency. Therefore, we propose a novel data-driven inertial navigation method based on federated learning, named FedINS, to improve IMU performance and solve the above-mentioned problems. In order to further improve performance and reduce hardware cost, we introduce the concept of segment fusion. FedINS2 (2 means the second S, which is the segment) is formed by combining FedINS with low-cost edge-end ranging, which has terminal computing capabilities and edge-end ranging capabilities. FedINS2 not only greatly improves the performance of the smartphone IMU from 3.6% to 0.8%, but also has the characteristics of privacy protection and efficient data collection. Experimental results demonstrate that the proposed data-driven inertial navigation algorithm is effective.

Deep learning has the potential to enhance both the performance and efficiency of indoor positioning using inertial measurement units (IMUs). Many existing deep learning methods for positioning rely on convolutional neural networks (CNNs), which are designed with increased depth to expand their receptive fields for capturing long-term features from IMU measurements. However, this design often introduces more parameters and computations, extracting redundant information. Conversely, shallow networks struggle to extract global features, resulting in unsatisfactory performance. In addition, in the era of the Internet of Things (IoT), the lightweighting of models and the high real-time performance of systems can effectively enhance the usability of portable devices and reduce their energy consumption, thereby enabling broader application. In this study, we introduce a lightweight network named ResMixer, capable of linearly capturing channel information and sequential features from IMU measurements. Despite maintaining the number of stages of a ResNet-style network, ResMixer significantly reduces the number of parameters—approximately a 70% reduction. Experimental results demonstrate that the proposed network achieves similar accuracy as ResNet18 while offering competitive performance compared to other lightweight models in terms of parameter count and computations. In our self-collected data, ResMixer demonstrates improvements in positioning accuracy and model efficiency compared to advanced algorithms such as LLIO and IMUNet. Additionally, we enhance the indoor positioning performance by combining measurements from additional single low-cost UWB, which further marks a 48.4% enhancement in precision in real scenario experiments compared to solely ResMixer.

The state of health is a crucial state that suggests the capacity of lithium-ion batteries to store and restitute energy at a certain power level, which should be carefully monitored in the battery management system. However, the state of health of batteries is unmeasurable and, currently, it is usually estimated within a specific area of the whole charging data, which is very limited in practical application because of the incomplete and random charging behaviors of users. In this paper, we intend to estimate the state of health of batteries with flexible partial charging curves and normal multi-layer perceptron based on the degradation data of eight 0.74 Ah batteries. To make the estimation more adaptive and flexible, we extract several features from partial charging curves. Analysis of the relationship between extracted features and the state of health shows that the extracted features are useful in estimation. As the length of the partial charging curve increases, the extracted features still function well, and the root mean square error of the test set is lower than 1.5%. Further validation on the other two types of batteries reveals that the proposed method achieves high accuracy even with different sampling and working conditions. The proposed method offers an easy-to-implement way to achieve an accurate estimation of a battery’s state of health.