With increasing computer-based work burden, prolonged poor sitting posture can result in health issues such as scoliosis. However, current sitting posture recognition systems often require the purchase of additional hardware. The camera-based system can compromise user privacy and be affected by varying lighting conditions. In this paper, we propose a solution to realize a sitting posture recognition system derived from acoustic signals generated by smartphones. Firstly, acoustic signals corresponding to various sitting postures are acquired via the built-in speaker and microphone of the smartphone. Subsequently, an innovative signal segmentation technique based on the adaptive threshold is designed to extract the signals, followed by the creation of a deep learning model for posture recognition. To meet the demands of lightweight deployment, a knowledge distillation compression technique is introduced to compress the model while maintaining its accuracy. The experimental results validate that our sitting posture recognition system has good effectiveness and robustness, making it more universal.

Choroidal thickness variations serve as critical biomarkers for numerous ophthalmic diseases. Accurate segmentation and quantification of the choroid in optical coherence tomography (OCT) images is essential for clinical diagnosis and disease progression monitoring. Due to the small number of disease types in the public OCT dataset involving changes in choroidal thickness and the lack of a publicly available labeled dataset, we constructed the Xuzhou Municipal Hospital (XZMH)-Choroid dataset. This dataset contains annotated OCT images of normal and eight choroid-related diseases. However, segmentation of the choroid in OCT images remains a formidable challenge due to the confounding factors of blurred boundaries, non-uniform texture, and lesions. To overcome these challenges, we proposed a mixed attention-guided multiscale feature fusion network (MAMFF-Net). This network integrates a Mixed Attention Encoder (MAE) for enhanced fine-grained feature extraction, a deformable multiscale feature fusion path (DMFFP) for adaptive feature integration across lesion deformations, and a multiscale pyramid layer aggregation (MPLA) module for improved contextual representation learning. Through comparative experiments with other deep learning methods, we found that the MAMFF-Net model has better segmentation performance than other deep learning methods (mDice: 97.44, mIoU: 95.11, mAcc: 97.71). Based on the choroidal segmentation implemented in MAMFF-Net, an algorithm for automated choroidal thickness measurement was developed, and the automated measurement results approached the level of senior specialists.

In the domain of smart devices, biometric identity authentication has become a leading and crucial technology, mainly because of its improved security and user convenience. Traditional methods frequently depend on complex activities, facial recognition, or passwords, which can be cumbersome and error-prone. This paper presents GrasOpen, an innovative biometric authentication system tailored for door-opening scenarios, utilizing the natural reach-and-grasp motion linked to door handles. The system employs smartwatches with accelerometers and gyroscopes to track and analyze arm movements, ensuring a seamless and intuitive user experience. GrasOpen tackles key challenges by leveraging the unique characteristics of the reach-and-grasp motion, eliminating the necessity for users to remember complex motions, and avoiding redundant actions like waiting for facial recognition. Specifically, GrasOpen initially proposes a lightweight model to discern door-opening actions from daily activities. Then, to realize the robust authentication system, GrasOpen integrates a complementary filter (CF) method to capture diversity-dependent features in door-opening actions. These features are subsequently processed through a modified ConvBoost model for precise user authentication. Experimental results reveal an impressive accuracy of 98.78% for activity recognition and 98.65% for identity authentication, highlighting GrasOpen’s excellence in function and performance. The system’s security and robustness are validated across diverse authentication environments.

Long-term incorrect sitting undoubtedly will damage physical health. Recognizing bad sitting posture has been of particular interest recently due to the prevailing Internet of Healthcare Things (IoHT). While various sitting posture recognition systems based on wearable devices and cameras are designed, they expose two obvious weaknesses. First, the sensors attached to the body will cause inconvenience to users, and using a camera requires high energy consumption and faces the risk of user privacy leakage. Second, most of these systems require massive training samples to build models, and the recognition performance of certain models on new user data with significant sample distribution differences remains poor. In this work, we propose SmartSit, the first-ever robust sitting posture recognition system with smartphone acoustic sensing. We start by designing a signal detection algorithm to determine the boundary of the sitting posture signal through a series of signal transformation methods. Then we construct the sitting posture recognition module MG-Reptile by modifying the meta-learning method by combining the Distributed Measurement Strategy (DMS) and Generative Adversarial Network (GAN). We show that the designed system is immune to the low generalization performance with only a few training samples. The observed testing results further validate the effectiveness and robustness of SmartSit.

Alzheimer’s disease (AD) is an insidious and progressive neurodegenerative disease, and the annual relevant social cost for AD patients can reach about $1 trillion worldwide. Therefore, early diagnosis and treatment of AD play a vital role in slowing disease progression. However, existing detection methods for cognitive impairment cannot consistently screen the stage of AD. To tackle this challenge, we propose an AD detection system, Ubi-AD, which combines the features of multiple biomarkers to realize passive and accurate AD detection. Unlike existing work, Ubi-AD can passively recognize the AD digital biomarkers during daily smartwatch usage without interfering with the user. At the user end, Ubi-AD first extracts the non-speech sounds (pause words, such as em, ah), which contain no privacy-sensitive content. Then, Ubi-AD recognizes the user’s walking activity, dining activity, and sleep activity from daily activities. Ubi-AD analyzes these data from smartwatch and predicts the AD stages using a multi-modal fusion neural network at the cloud end. We evaluate our model on a collected dataset from 45 volunteers. As a result, Ubi-AD can reach a detection accuracy of 93.4%, which means that Ubi-AD can provide multiple effective biomarkers for ubiquitous and passive detection in daily life.

The blockchain with a distributed network structure can provide a reliable and secure environment for Internet of Things (IoT) transactions, which also suffers from low throughput, high computation overhead, and large transaction fee. Payment channel networks (PCNs) are developed to address the scalability issue of blockchain. A key enabler of PCNs is the path-finding services. Most of existing routing algorithms target at finding a single feasible path, which may lead to failure of large payments. Moreover, previous solutions did not consider the transaction fee of the chosen path, which is extremely important to cost-sensitive users. In this work, we design a new multipath routing algorithm for PCNs that aims at minimizing transaction fees. Together considering the determination of the optimal number of paths, the optimal path routes and the optimal allocation leads to difficulty of the problem. In addition, the transaction fees along the path are closely related to the amount of payment, and the capacity of a payment channel limits the payment that can be carried. To address these challenges, we propose MILPA-PCN, a cost-effective multipath routing framework for PCNs. We develop a genetic algorithm-based routing determination algorithm with carefully designed genetic operations. We evaluate MILPA-PCN based on the real trace of the lightning network, and verify that MILPA-PCN can reduce the transaction fee by 33.56% and improve the payment success rate by 14.45%.

With the popularity of personal computing devices, people often keep long-term head immobility in front of screens, resulting in the emergence of “phubbers” and “office workers.” The early warning solutions in the Internet of Healthcare Things (IoHT) have brought hope to protect users’ health and safety. However, most existing works cannot recognize the different head gestures during walking, which is also a common cause of text neck and traffic accidents. In addition, they also need a large amount of data to update the model to adapt to the new environment, which reduces the practicality of the model. To solve these problems, we propose a system, CSEar, based on built-in accelerometers of off-the-shelf wireless earphones, which can recognize 12 kinds of head gestures both in resting and walking states. First, an innovative algorithm is designed to detect head gesture signals, especially for the signals mixed with gait. Then, we propose the MetaSensing, a head gesture recognition model that can improve the recognition ability with few samples compared with the existing metalearning algorithms. Finally, the experimental results prove the effectiveness and robustness of the CSEar.

The rapid development of the information-centric wireless sensor network (ICWSN) has solved the challenges of information transmission and processing caused by the accelerated growth of wearable devices and the wide deployment of the Internet of Things (IoT) recently. The privacy security is also a growing problem. The existing works use earphones, covert, and user-friendly wearable devices, for user authentication. However, some of the earphone-based authentication solutions need to customize special earphones, which are not universal. Other solutions use microphones and speakers of earphones for authentication, which are susceptible to changes in the auricle’s internal environment, resulting in a decline in performance. To solve this problem, a new authentication solution based on the existing commercial earphones is proposed to authenticate a user by tapping on the earphone rhythmically. This rhythmic tap behavior causes a change of the signal waveform of the built-in accelerometer in the earphone. Based on this, we design a pipeline to authenticate the user’s identity. We first design an event detection algorithm to segment the tap signal accurately. Then, we use the global features calculated based on the event detection algorithm and local features extracted from the convolutional neural network (CNN) for building an authentication model using the Naive Bayes (NB) classifier. Finally, 20 users are recruited to evaluate the experiment and the recognition accuracy reaches 98%. Moreover, we extend the experiment to prove that it has a good performance against the different attacks and is robust in different scenarios.

With the development of the industrial Internet of Things (IIoT), intelligent healthcare aims to build a platform to monitor users’ health-related information based on wearable devices remotely. The evolution of blockchain and artificial intelligence technology also promotes the progress of secure intelligent healthcare. However, since the data are stored in the cloud server, it still faces the risk of being attacked and privacy leakage. Note that little attention has been paid to the security issue of privacy information mixed in raw data collected from large number of distributed and heterogeneous wearable healthcare devices. To solve this problem, in this article, we design a deep learning-based privacy preservation and data analytics system for IoT enabled healthcare. At the user end, we collect raw data and separate the users’ privacy information in the privacy-isolation zone. At the cloud end, we analyze the health-related data without users’ privacy information and construct a delicate security module based on the convolutional neural network. We also deploy and evaluate the prototype system, where extensive experiments prove its effectiveness and robustness.

Blockchain-based cryptocurrencies are severely limited in transaction throughput and latency. A promising solution to this issue is a payment channel, which allows trust-free payments between two peers without exhausting the resources of the blockchain. A linked payment channel network (PCN) enables payments between two peers through a series of intermediate nodes that forward and charge for the payments. However, most of existing proposals only use the shortest path as the path of the transaction, which causes the frequently reused channels to be exhausted quickly. In addition, most of existing PCNs are almost only designed for payments between two parties, which leads to limited application scenarios. When multiple payments use the same intermediate channel, the two-party PCNs cannot achieve simultaneous payments. In this paper, we propose a multi-party payment channel (MPC) network, a payment channel proposal that supports multiple payments using the same intermediate channel simultaneously, thereby greatly expanding the application scenarios of payment channels. In addition, our channel selection and transaction conversion strategies can also increase the success rate of transactions. We implement MPC network in the simulated blockchain network and lightning network based on Truffle, and a large number of experiments verify the effectiveness of our solution.

Following the correct stroke order while writing Chinese characters composed of strokes plays an important role in handwriting efficiency and quality, especially for early education. Most existing systems use image processing techniques for character and stroke order recognition, which is sensitive to lighting conditions. In this paper, we present the design, implementation and evaluation of SmartSO, which utilizes the inertial sensors of an off-the-shelf smartwatch for Chinese character and stroke order recognition. SmartSo first identifies the Chinese character written by the user, based on which SmartSo decides whether the stroke order is written correctly to help improve users' writing behavior. The biggest challenge for stroke order recognition is that some Chinese characters have repeated strokes (strokes of the same type), e.g., with two same horizontal strokes, and it is challenging to differentiate the writing order of such strokes given only the detected stroke composition (number and type of strokes). To mitigate this problem, we further analyze the hand movement between two adjacent strokes (referred to as direction motion) and propose a novel algorithm to recognize stroke order based on direction motion information. Finally, we build a fully functional prototype of SmartSO, and extensive experiments confirm its effectiveness and robustness.

Most existing systems use portable devices or image processing techniques for handwritten Chinese character recognition (HCCR), which are unable to detect character when writing on a paper or sensitive to lighting conditions. In this article, we present the design, implementation, and evaluation of a smartwatch-based HCCR system, called SmartHandwriting. To segment each Chinese character, we further analyze the hand movement between the handwriting gesture and the wrist movement gesture and propose a novel algorithm to distinguish the two types of gestures. Due to too many Chinese characters for classification, we utilize the data augmentation method for avoiding overfitting. Then, we build the HCCR model using the deep convolutional neural network (DCNN) method. The recognition accuracy of the Chinese characters is 96.0%, and extensive experiments confirm its effectiveness and robustness. Moreover, we also explore adverse factors that affect the recognition performance, which can be avoided in the future.

Performing the correct pen-holding gesture plays an important role in handwriting efficiency and quality, especially for early education. In this paper, we present the design, implementation and evaluation of SmartWriting, which utilizes the smartwatch for pen-holding gesture recognition for both Chinese and English writing. SmartWriting can automatically identify whether the user is writing Chinese or English, based on which two classifiers are built to detect pen-holding gestures by strokes in Chinese characters or English letters. In particular, we propose to leverage the combined signal of one vertical stroke and one horizontal stroke for efficient pen-holding gesture identification in Chinese writing, and we are able to infer pen-holding gesture according to any letter in English writing. We build a fully functional prototype of SmartWriting, and extensive experiments confirm its effectiveness and robustness. The detection accuracy is 9. % and 9 % for Chinese and English respectively. SmartWriting provides a natural, convenient and inexpensive way to improve users' writing habits.