The density of a single cell is a fundamental property of cells. Cells in the same cycle phase have similar volume, but the differences in their mass and density could elucidate each cell's physiological state. Here we report a novel technique to rapidly measure the density and mass of a single cell using an optically induced electrokinetics (OEK) microfluidic platform. Presently, single cellular mass and density measurement devices require a complicated fabrication process and their output is not scalable, i.e., it is extremely difficult to measure the mass and density of a large quantity of cells rapidly. The technique reported here operates on a principle combining sedimentation theory, computer vision, and microparticle manipulation techniques in an OEK microfluidic platform. We will show in this paper that this technique enables the measurement of single-cell volume, density, and mass rapidly and accurately in a repeatable manner. The technique is also scalable – it allows simultaneous measurement of volume, density, and mass of multiple cells. Essentially, a simple time-controlled projected light pattern is used to illuminate the selected area on the OEK microfluidic chip that contains cells to lift the cells to a particular height above the chip's surface. Then, the cells are allowed to “free fall” to the chip's surface, with competing buoyancy, gravitational, and fluidic drag forces acting on the cells. By using a computer vision algorithm to accurately track the motion of the cells and then relate the cells' motion trajectory to sedimentation theory, the volume, mass, and density of each cell can be rapidly determined. A theoretical model of micro-sized spheres settling towards an infinite plane in a microfluidic environment is first derived and validated experimentally using standard micropolystyrene beads to demonstrate the viability and accuracy of this new technique. Next, we show that the yeast cell volume, mass, and density could be rapidly determined using this technology, with results comparable to those using the existing method suspended microchannel resonator.

The Industrial Internet of Things (IIoT) connects industrial assets to ubiquitous smart sensors and actuators to enhance manufacturing and industrial processes. Data-driven condition monitoring is an essential technology for intelligent manufacturing systems to identify anomalies from malfunctioning equipment, prevent unplanned downtime, and reduce the operation costs by predictive maintenance without interrupting normal machine operations. However, data-driven condition monitoring requires massive data collected from smart sensors to be transmitted to the cloud for further processing, thereby contributing to network congestion and affecting the network performance. Furthermore, unbalanced training data with very few labelled anomalies limit supervised learning models because of the lack of sufficient fault data for the training process in anomaly detection algorithms. To address these issues, we proposed an IIoT-based condition monitoring system with an edge-to-cloud architecture and computed the relative wavelet energy as feature vectors on the edge layer to reduce the network traffic overhead. We also proposed an unsupervised deep long short-term memory (LSTM) network module for anomaly detection. We implemented the proposed IIoT condition monitoring system for a manufacturing machine in a real shop site to evaluate our proposed solution. Our experimental results verify the effectiveness of our approach which can not only reduce the network traffic overhead for the IIoT but also detect anomalies accurately.

We present a method capable of rapidly (∼20 s) determining the density and mass of a single leukemic cell using an optically induced electrokinetics (OEK) platform. Our team had reported recently on a technique that combines sedimentation theory, computer vision, and micro particle manipulation techniques on an OEK microfluidic platform to determine the mass and density of micron-scale entities in a fluidic medium; the mass and density of yeast cells were accurately determined in that prior work. In the work reported in this paper, we further refined the technique by performing significantly more experiments to determine a universal correction factor to Stokes' equation in expressing the drag force on a microparticle as it falls towards an infinite plane. Specifically, a theoretical model for micron-sized spheres settling towards an infinite plane in a microfluidic environment is presented, and which was validated experimentally using five different sizes of micro polystyrene beads. The same sedimentation process was applied to two kinds of leukemic cancer cells with similar sizes in an OEK platform, and their density and mass were determined accordingly. Our tests on mouse lymphocytic leukemia cells (L1210) and human leukemic cells (HL-60) have verified the practical viability of this method. Potentially, this new method provides a new way of measuring the volume, density, and mass of a single cell in an accurate, selective, and repeatable manner.

A Micro Inertial Measurement Unit ("IMU) which is based on MEMS accelerometers and gyro sensors is devel- oped for real-time recognition of human hand motions, which when combined with appropriate filtering and transformation algorithms, becomes a Digital Writing System that can be used to record handwriting on any surface. The overall size of our "IMU is less than 25mm £ 70mm £ 20mm, including the micro sensors, processor, and wireless interface components. We present our progress on using this "IMU based on Kalman filtering algorithm to filter the noise of sensors, which has allowed the system to successfully transform hand motions into recognizable and recordable English characters. Our goal is to implement this system to a digital hand-writing system that will interface with PC and mobile computing devices.

Arrhythmia is a marked symptom of many cardiovascular diseases (CVDs). Fast and accurate measurement of heart rate (HR) can lead to prompt detection of arrhythmia, which could be potentially life-threatening. However, it remains a challenge to robustly and remotely measure HR due to the typically changing ambient conditions surrounding human subjects. In this study, we propose a method to accurately measure human HR by simply using a smartphone camera. We use three special approaches for HR estimation. Firstly, we fuse the raw data by subtracting G channel from R channel to enhance the heartbeat signal from video data. Secondly, instead of the entire palm, we select a region of interest (ROI) from the palm for heart rate detection based on signal-to-noise ratio (SNR) maps, which reduces the proportion of the weak signal area. Thirdly, after applying Fast Fourier Transform (FFT) analyses of the time-series data from the video, we set an interval threshold based on the heart rate range to accurately determine heart rate. With a reference HR value measured by electrocardiography (ECG), the detection rate of the proposed method is 95.84% (overall result from 30 test subjects) when used for measurement at an ambient light intensity of 150 lux within a distance of 4.5 meters. The method’s strong adaptability to changing ambient conditions and ease of implementation makes it applicable to many scenarios, such as homes of the elderly, classrooms, and other public spaces.

Sports performance is often judged based on the results of a series of motions rather than observing and analyzing the detailed sequential motions that lead to the results. Hence, subjective feedback from the coaches is often ineffective in improving player performance. In this work, we custom-built a smart Internet of Things wristband motion sensor to implement data-based sports performance evaluation. A phase-based feature selection method is also proposed to assess the athletes’ sequential detailed motion for selected sport activities. To demonstrate the merits of this technology, we quantified the quality of the sequential motions of a specific type of volleyball serve by analyzing 183 samples of motion data obtained from a total of 18 players. The general skill levels (i.e., elite, subelite, and amateur) of the players were identified by machine learning algorithms, with accuracies of up to 95%. Moreover, we adopted biomechanical principles to extract 11 motion-related performance metrics from various phases of the players’ serve motion. We identified the distributions of these metrics across different skill levels and found eight key metrics that were highly correlated to the skill level of a players. We suggest that these metric distributions can be used as a reference for providing feedback to the coaches and players, to improve a player’s skill in the future. This phased-based analysis method can potentially be applied across many sports to increase the effectiveness of athletes’ training.