Mobile-cloud collaborative Convolutional Neural Network (CNN) inference enables the efficient execution of CNN models by offloading partial inference workloads from mobile devices to the cloud. Although model partitioning for collaborative inference has been extensively studied, most existing approaches assume reliable mobile-cloud transmission, which often breaks down in real-world wireless environments with packet loss. In such scenarios, incomplete feature transmission can result in a significant drop in inference accuracy. In this paper, a joint scheduling approach is proposed to address this challenge, leveraging a search-based algorithm to determine both the model partition layer and redundancy level, to balance inference accuracy and latency under packet loss conditions. The proposed method is evaluated in a real-world mobile-cloud environment. Results show that it reduces the inference latency by up to 30.3% compared to the non-redundant baseline with the same accuracy threshold.

Cuff-less methods, especially pulse wave analysis (PWA) techniques with PPG/mmWave sensing, have shown great potential for non-intrusive blood pressure (BP) monitoring. However, the state-of-the-art solutions are only validated on small-scale healthy subjects, neglecting patients with abnormal BP and thus a more urgent need for BP monitoring. To bridge the gap, we first build the largest mmWave-BP dataset to our knowledge, including 930 real patients with cardiovascular diseases, and perform extensive experiments, which reveals that all existing PWA methods exhibit far less satisfactory performance with standard deviation errors (STD) exceeding 16 mmHg for systolic BP (SBP) and 11mmHg for diastolic BP (DBP). An in-depth investigation shows that physiological factors have complex effect on vascular elasticity and structure, thus people with very different BP values may exhibit extremely similar pulse waveform, which leads to confusion in model learning. In this work, we propose BP3, which fuses physiological factors into sensing-data-driven deep-learning framework, so as to capture the intricate effect of physiological factors during the whole process of learning pulse waveforms. Evaluation results show that BP3 achieves the mean errors of-1.57 mmHg and -0.34 mmHg, STD of 9.77 mmHg and 7.93 mmHg for SBP and DBP, respectively. Moreover importantly, BP3 shows remarkable gain particularly for subjects with abnormal BP, achieving mean errors that are only 0.48% ~ 20.86% of the state-of-the-art solutions.

Wireless sensing offers a promising approach to identify the content of liquids without opening the container or directly touching the liquid. Although existing methods aim to achieve fine-grained identification, i.e., distinguishing a 1% v/v difference in alcohol content, they still have limitations in detecting highly deceptive counterfeit liquors that have much smaller content differences, sometimes as low as 0.2% v/v alcohol content. In this paper, we propose mm Taster, a mobile system that combines the mmWave radar with a smartphone to perform fine-grained and robust alcohol sensing. To achieve the desired fine granularity, we introduce a novel feature extraction model that exploits theunique reflection responses across multiple mmWave frequencies, which provide discriminative information about liquid content. Furthermore, we observe the serious interference of target displacement on identification performance, which hinders the various applications in mobile scenarios. To enhance the robustness, mm Taster incorporates a customizedtranslation-invariantneural network,ConvNet, to remove the location interference and extract stable liquid-dependent features regardless of target displacement. Extensive experimental results demonstrate that mm Taster can accurately distinguish the alcohol differences as low as0.2% v/vwith an accuracy of over90.8%even in scenarios involving diverse displacements and rotations.

Gait recognition based on millimeter waves (mmWave) can recognize people's identity information by sensing their walking posture, which has found versatile usages in many fields, such as smart home, intelligent security, and health monitoring. While this technology has gained extensive attention in recent years, its possibility of being misused is also increasing. The snooper who misuses the technology could monitor the victim's identity information, which is imperceptible due to the characteristics of mmWave-based gait recognition. In this paper, we propose an identity protector called WW-IDguard, which disrupts the snooper at the physical level. The key idea is that the protector sends a unique signal to interfere with not only the signal but also the gait feature of the person “seen” by the snooper. Experiments demonstrate that WW-IDguard can significantly reduce the accuracy of the mmWave-based gait recognition used by snoopers. We also perform a measurement analysis on the basic method.

Respiratory rate (RR) is of great value in health care, especially when it can be continuously monitored using wearable devices in daily life. Recent works employ photoplethysmography (PPG) on smartwatch for continuous respiration monitoring, based on a certain medical discovery called respiratory sinus arrhythmia (RSA), which describes the relationship between respiratory and heart rate. However, we find that these works fall short of robustness. In particular, the respiratory estimation accuracy drops significantly when people breathe faster (e.g., after sports). We further identify the root reason that the RSA gradually weakens as the RR increases. In this article, we propose BreathAnalyzer, which can estimate RR accurately even at high RRs. To achieve this, BreathAnalyzer boosts the weakened RSA and also handles the motion artifacts, by integrating features from multiple domains, i.e., frequency, time, and nonlinear Poincare domain, instead of using the single spectrum or raw signal in previous studies. Moreover, BreathAnalyzer custom-designs a tree-based learning model, which fits multidomain features, while considering limitations of smartwatch. We implement BreathAnalyzer prototype on COTS smartwatch, and extensive evaluation demonstrates that BreathAnalyzer outperforms the state-of-the-art approaches, with accuracy improvement by 35.37%–80.42% across a variety of practical scenarios including high RRs.

Blood pressure (BP), an important vital sign to assess human health, is expected to be monitored conveniently. The existing BP monitoring methods, either traditional cuff based or newly emerging wearable based, all require skin contact, which may cause unpleasant user experience and is even injurious to certain users. In this article, we explore contactless BP monitoring and propose airBP, which emits millimeter-wave signals toward a user’s wrist, and captures the reflected signal bounded off from the pulsating artery underlying the wrist. By analyzing the reflected signal strength of the signal, airBP generates the arterial pulse and further estimates BP by exploiting the relationship between the arterial pulse and BP. To realize airBP, we design a new beam-forming method to keep focusing on the tiny and hidden wrist artery, by leveraging the inherent periodicity of the arterial pulse. Moreover, we custom design a pre-training and neural network architecture, to combat the challenges from the arterial pulse sparsity and ambiguity, so as to estimate BP accurately. We prototype airBP using a coin-size commercial off-the-shelf millimeter-wave radar and perform extensive experiments on 41 subjects. The results demonstrate that airBP accurately estimates systolic and diastolic BP, with a mean error of –0.30 mmHg and –0.23 mmHg, as well as a standard deviation error of 4.80 mmHg and 3.79 mmHg (within the acceptable range regulated by the FDA’s AAMI protocol), respectively, at a distance up to 26 cm.

Affective computing has attracted researchers' attention in recent years. Emotion recognition is part of affective computing, which aims to recognize how the person feels, such as happy, sad, anger, disgust, fear, surprise. Traditional works about emotion recognition mainly focus on the characteristic of the person itself, such as audio, text, facial expression, body posture. However, the feelings of people can easily be affected by the context information. In this paper, we utilize the context to construct an affective graph to reason the emotional states. In detail, we detect the context using Region Proposal Network (RPN) to extract nodes as the input of the Graph Convolution Network (GCN), which transfers the convolution operation from Euclidean data structure to nonEuclidean data structure. The GCN learns the affective relationship during the back-propagation process. Moreover, the body feature is extracted by Convolution Neural Network (CNN). The output of GCN and CNN are combined finally to infer the discrete emotion categories and the continuous dimensions for VAD (Valence, Arouse, Dominance) measurement. Our method achieves higher performance than the baseline based on the EXOTIC dataset.

Crowdsourcing provides a novel and effective way of constructing a location image database for mobile visual location recognition. Compared with traditional location image databases, a crowdsourced database has richer information for location images, with various angles, times, distances and weathers, providing great potential for high recognition accuracy. However, it is inevitable to have various disturbances on these location images, hindering the potential. To address this challenge, we first propose a Common Crucial Feature (CCF) detection algorithm to exclude unimportant visual features from crucial features. To achieve a good balance between the efficiency and accuracy, we further propose a CCF based Visual Hash Bits (VHB) scheme to encode CCF features into hash bits to vote for most matching images. Extensive experiments are conducted on a crowdsourced dataset with 9,064 location images, demonstrating that our scheme outperforms other state-of-the-art schemes.