Parkinson's disease (PD) is a chronic neurodegenerative disorder resulting from the progressive loss of dopaminergic nerve cells. People with PD usually demonstrate deficits in performing basic daily activities, and the relevant annual social cost can reach about $25 billion in the United States. Early detection of PD plays an important role in symptom relief and improvement in the performance of activities in daily life (ADL), which eventually reduces societal and economic burden. However, conventional PD detection methods are inconvenient in daily life (e.g., requiring users to wear sensors). To overcome this challenge, we propose and identify the non-speech body sounds as the new PD biomarker, and utilize the data in smartphone usage to realize the passive PD detection in daily life without interrupting the user. Specifically, we present PDVocal, an end-to-end smartphone-based privacy-preserving system towards early PD detection. PDVocal can passively recognize the PD digital biomarkers in the voice data during daily phone conversation. At the user end, PDVocal filters the audio stream and only extracts the non-speech body sounds (e.g., breathing, clearing throat and swallowing) which contain no privacy-sensitive content. At the cloud end, PDVocal analyzes the body sounds of interest and assesses the health condition using a customized residual network. For the sake of reliability in real-world PD detection, we investigate the method of the performance optimizer including an opportunistic learning knob and a long-term tracking protocol. We evaluate our proposed PDVocal on a collected dataset from 890 participants and real-life conversations from publicly available data sources. Results indicate that non-speech body sounds are a promising digital biomarker for privacy-preserving PD detection in daily life.

The rapid development of Internet of Things is yielding a huge volume of time series data, the real-time mining of which becomes a major load for data centers. The computation bottleneck in time series mining is the distance measure, in which dynamic time warping (DTW) is one of the most widely used distance measures. Recently, various software optimization and hardware acceleration techniques have been proposed for DTW acceleration. However, the throughput and energy efficiency of DTW are still big concerns considering the ever-increasing volume of times series. In this paper, we propose a high-throughput and efficient memristor-based DTW architecture for real-time time series mining on data centers. Specifically, memristors have been adopted for both computation and configuration of the computing architecture. The computation flow in this architecture is fully presented in a continuous and asynchronous manner. To improve the computation efficiency, we propose an early lower bound algorithm by exploiting the predictability in the circuit characteristic. Experiments are performed with module evaluation and end-to-end evaluation including three popular applications: 1) similarity search; 2) classification; and 3) anomaly detection. Experimental results indicate that, compared to existing approaches, the speedup and energy efficiency improvement are 12x-43x and 51x-287x, respectively.

As a micro-engineered biomimetic system to replicate key functions of living organs, organ-on-a-chip (OC) technology provides a high-throughput model for investigating complex cell interactions with both high temporal and spatial resolutions in biological studies. Typically, microscopy and high-speed video cameras are used for data acquisition, which are expensive and bulky. Recently, compressed sensing (CS) has increasingly attracted attentions due to its extremely low-complexity structure and low sampling rate. However, there is no CS solution tailored for tempo-spatial information acquisition. In this paper, we propose tempo-spatial CS (TS-CS), a unified CS architecture for OC stream, which achieves significant cost reduction and truly combines sensing with compression along the temporal and spatial domains. We point out that TS-CS can consistently achieve better performance by exploiting tempo-spatial compressibility in OC data. To this end, we comprehensively evaluate the system performance by employing four different bases for CS. With comparison to the traditional way, we show that TS-CS always obtains better recovery result with a throughput bound and can achieve around throughput improvement under a reconstruction demand by applying discrete cosine transform matrix as the basis.

Recently, continuous 3D printing, a revolutionary branch of legacy additive manufacturing, has made its two-order time efficiency breakthrough in industrial manufacturing. As its manufacturing technique advances rapidly, the prefabrication to slice the 3D object into image layers becomes potential to impede further improvement of production efficiency. In this paper, we present two scalable and efficient graphic processing unit (GPU) enabled schemes, i.e., pixelwise parallel slicing and fully parallel slicing, to accelerate the image-projection based slicing algorithm in continuous 3D printing. Specifically, the pixelwise approach utilizes the pixel-level parallelism and exploits the in-shared-memory computing on GPU. The fully parallel method aggressively expands the parallelism on both triangle mesh size and slicing layers. The thread-level priority competing issue, resulting from full parallelism, is addressed by a critical area using atomic operation. Experiments with real 3D object benchmarks show that our pixelwise parallel slicing can gain one order of magnitude runtime reduction to CPU, and the fully parallel slicing achieves two orders improvement. We also evaluate the scalability of both proposed schemes.

Wearable computing systems have spurred many opportunities to continuously monitor human bodies with sensors worn on or implanted in the body. These emerging platforms have started to revolutionize many fields, including healthcare and wellness applications, particularly when integrated with intelligent analytic capabilities. However, a significant challenge that computer architects are facing is how to embed sophisticated analytic capabilities in wearable computers in an energy-efficient way while not compromising system performance. In this paper, we present XPro, a novel cross-end analytic engine architecture for wearable computing systems. The proposed cross-end architecture is able to realize a generic classification design across wearable sensors and a data aggregator with high energy-efficiency. To facilitate the practical use of XPro, we also develop an Automatic XPro Generator that formally generates XPro instances according to specific design constraints. As a proof of concept, we study the design and implementation of XPro with six different health applications. Evaluation results show that, compared with state-of-the-art methods, XPro can increase the battery life of the sensor node by 1.6-2.4X while at the same time reducing system delay by 15.6-60.8% for wearable computing systems.

Inkjet 3D printing is a disruptive manufacturing technology in emerging metal- and bio-printing applications. The nozzle of the printer deposits tiny liquid droplets, which are subsequently solidified on a target location. Due to the elegant concept of micro-droplet deposition, inkjet 3D printing is capable of achieving a sub-millimeter scale manufacturing resolution. However, the droplet deposition process is dynamic and uncertain which imposes a significant challenge on quality assurance of inkjet 3D printing in terms of product reproducibility and process repeatability. To this end, we present Luban as a certification tool to examine the printing quality in the inkjet printing process. Luban is a new low-cost and in-situ droplet micro-sensing system that can precisely detect, analyze and localize a droplet. Specifically, we present a novel tiny object sensing method by exploiting the computational light beam field and its sensitive interference effect. The realization of Luban is associated with two technical thrusts. First, we study integral sensing, i.e., a new scheme towards computational light beam field sensing, to efficiently extract droplet location information. This sensing scheme offers a new in-situ droplet sensing modality, which can promote the information acquisition efficiency and reduce the sensing cost compared to prior approaches. Second, we characterize interference effect of the computational light beam field and develop an efficient integration-domain droplet location estimation algorithm. We design and implement Luban in a real inkjet 3D printing system with commercially off-the-shelf devices, which costs less than a hundred dollars. Experimental results in both simulation and real-world evaluation show that Luban can reach the certification precision of a sub-millimeter scale with a 99% detection accuracy of defect droplets; furthermore, the enabled in-situ certification throughput is as high as over 700 droplets per second. Therefore, the performance of our Luban system can meet the quality assurance requirements (e.g., cost-effective, in-situ, high-accuracy and high-throughput) in general industrial applications.

Nurses regularly perform patient handling activities. These activities with awkward postures expose healthcare providers to a high risk of overexertion injury. The recognition of patient handling activities is the first step to reduce injury risk for caregivers. The current practice on workplace activity recognition is based on human observational approach, which is neither accurate nor projectable to a large population. In this paper, we aim at addressing these challenges. Our solution comprises a smart wearable device and a novel spatio-temporal warping (STW) pattern recognition framework. The wearable device, named Smart Insole 2.0, is equipped with a rich set of sensors and can provide an unobtrusive way to automatically capture the information of patient handling activities. The STW pattern recognition framework fully exploits the spatial and temporal characteristics of plantar pressure by calculating a novel warped spatio-temporal distance, to quantify the similarity for the purpose of activity recognition. To validate the effectiveness of our framework, we perform a pilot study with eight subjects, including eight common activities in a nursing room. The experimental results show the overall classification accuracy achieves 91.7%. Meanwhile, the qualitative profile and load level can also be classified with accuracies of 98.3% and 92.5%, respectively.

As mobile technology grows rapidly, the smartphone has become indispensable for transmitting private user data, storing the sensitive corporate files, and conducting secure payment transactions. However, with mobile security research lagging, smartphones are extremely vulnerable to unauthenticated access. In this paper, we present, EyeVeri, a novel eye-movement-based authentication system for smartphone security protection. Specifically, EyeVeri tracks human eye movement through the built-in front camera and applies the signal processing and pattern matching techniques to explore volitional and non-volitional gaze patterns for access authentication. Through a comprehensive user study, EyeVeri performs well and is a promising approach for smartphone user authentication. We also discuss the evaluation results in-depth and analyze opportunities for future work.

In recent years, the analog-to-information converter (AIC), based on compressed sensing (CS) paradigm, is a promising solution to overcome the performance and energy-efficiency limitations of traditional analog-to-digital converters (ADC). Especially, AIC can enable sub-Nyquist signal sampling proportional to the intrinsic information in biomedical applications. However, the legacy AIC structure is tailored toward specific applications, which lacks of flexibility and prevents its universality. In this paper, we introduce a novel programmable AIC architecture, Pro-AIC, to enable effective configurability and reduce its energy overhead by integrating efficient multiplexing hardware design. To improve the quality and time-efficiency of Pro-AIC configuration, we also develop a rapid configuration algorithm, called RapSpiral, to quickly find the near-optimal parameter configuration in Pro-AIC architecture. Specifically, we present a design metric, trade-off penalty, to quantitatively evaluate the performance-energy trade-off. The RapSpiral controls a penalty-driven shrinking triangle to progressively approximate to the optimal trade-off. Our proposed RapSpiral is with log(n) complexity yet high accuracy, without pretraining and complex parameter tuning procedure. RapSpiral is also probable to avoid the local minimum pitfalls. Experimental results indicate that our RapSpiral algorithm can achieve more than 30x speedup compared with the brute force algorithm, with only about 3% trade-off compromise to the optimum in Pro-AIC. Furthermore, the scalability is also verified on larger size benchmarks.

Gait analysis is an important medical diagnostic process and has many applications in healthcare, rehabilitation, therapy, and exercise training. However, typical gait analysis has to be performed in a gait laboratory, which is inaccessible for a large population and cannot provide natural gait measures. In this paper, we present a novel sensor device, namely, Smart Insole, to tackle the challenge of efficient gait monitoring in real life. An array of electronic textile (eTextile)-based pressure sensors are integrated in the insole to fully measure the plantar pressure. Smart Insole is also equipped with a low-cost inertial measurement unit including a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer to capture the gait characteristics in motion. Smart Insole can offer precise acquisition of gait information. Meanwhile, it is lightweight, thin, and comfortable to wear, providing an unobtrusive way to perform the gait monitoring. Furthermore, a smartphone graphic user interface is developed to display the sensor data in real-time via Bluetooth low energy. We perform a set of experiments in four real-life scenes including hallway walking, ascending/descending stairs, and slope walking, where gait parameters and features are extracted. Finally, the limitation and improvement, wearability and usability, further work, and healthcare-related potential applications are discussed.

The past decades have witnessed a rapid surge in new sensing and monitoring devices for well-being and healthcare. One key representative in this field is body sensor networks (BSNs). However, with advances in sensing technologies and embedded systems, wireless communication has gradually become one of the dominant energy-consuming sectors in BSN applications. Recently, compressed sensing (CS) has attracted increasing attention in solving this problem due to its enabled sub-Nyquest sampling rate. In this paper, we investigate the quantization effect in CS architecture and argue that the quantization configuration is a critical factor of the energy efficiency for the entire CS architecture. To this end, we present a novel configurable quantized compressed sensing (QCS) architecture, in which the sampling rate and quantization are jointly explored for better energy efficiency. Furthermore, to combat the computational complexity of the configuration procedure, we propose a rapid configuration algorithm, called RapQCS. According to the experiments involving several categories of real biosignals, the proposed configurable QCS architecture can gain more than 66% performance-energy tradeoff than the fixed QCS architecture. Moreover, our proposed RapQCS algorithm can achieve over 150× speedup on average, while decreasing the reconstructed signal fidelity by only 2.32%.

The increasing popularity of mobile devices has brought severe challenges to device usability and big data analysis. In this paper we investigate the intellectual recommender system on cell phones by incorporating mobile data analysis. Nowadays with the development of smart phones, more and more applications have emerged on various areas, such as entertainment, education and health care. While these applications have brought great convenience to people's daily life, they also provide tremendous opportunities for analyzing users' interests. In this work we develop an Android background service to collect the user behaviors and analyze their preferences based on their Android application usage. As one of the most intuitive media for visual representation, videos with various types of contents are recommended to users based on a proposed graphical model. The proposed model jointly utilizes the textual descriptions of Android applications and videos, as well as the extracted video content based features. Besides, by analyzing the user's habit of application usage we seamlessly integrate the user's personal interests during the recommendation. The extensive comparisons to multiple baselines reveal the superiority of the proposed model on the recommendation quality. Furthermore, we conduct experiments on personalized recommendation to demonstrate the capacity of the proposed model in effectively analyzing the user's personal interests.

Sleep monitoring is receiving increased attention in the healthcare community, because the quality of sleep has a great impact on human health. Existing in-home sleep monitoring devices are either obtrusive to the user or cannot provide adequate sleep information. To this end, we present SleepSense, a contactless and low-cost sleep monitoring system for home use that can continuously detect the sleep event. Specifically, SleepSense consists of three parts: an electromagnetic probe, a robust automated radar demodulation module, and a signal processing framework for sleep event recognition, including on-bed movement, bed exit, and breathing event. We present a prototype of the SleepSense system, and perform a set of comprehensive experiments to evaluate the performance of sleep monitoring. Using a real-case evaluation, experimental results indicate that SleepSense can perform effective sleep event detection and recognition in practice.

Wireless sensor nodes advance the brain-computer interface (BCI) from laboratory setup to practical applications. Compressed sensing (CS) theory provides a sub-Nyquist sampling paradigm to improve the energy efficiency of electroencephalography (EEG) signal acquisition. However, EEG is a structure-variational signal with time-varying sparsity, which decreases the efficiency of compressed sensing. In this paper, we present a new adaptive CS architecture to tackle the challenge of EEG signal acquisition. Specifically, we design a dynamic knob framework to respond to EEG signal dynamics, and then formulate its design optimization into a dynamic programming problem. We verify our proposed adaptive CS architecture on a publicly available data set. Experimental results show that our adaptive CS can improve signal reconstruction quality by more than 70% under different energy budgets while only consuming 187.88 nJ/event. This indicates that the adaptive CS architecture can effectively adapt to the EEG signal dynamics in the BCI.