Chengshuo Xia 교수

Energy harvesting (EH) technique has been proposed as a favorable solution for addressing the power supply exhaustion in a wireless sensor node and prolong the operating time for a wireless sensor network. Thermoelectric energy generator (TEG) is a valuable device converting the waste heat into electricity which can be collected and stored for electronics. In this paper, the thermal energy from human body is captured and converted to the low electrical energy by means of thermoelectric energy ha

Human activity recognition (HAR) systems combined with machine learning normally serve users based on a fixed sensor position interface. Variations in the installing position will alter the performance of the recognition and will require a new training dataset. Therefore, we need to understand the role of sensor position in HAR system design to optimize its effect. In this paper, we designed an optimization scheme with virtual sensor data for the HAR system. The system is able to generate the op

The performance of a fixed-sensor-based hand gesture recognition system is typically influenced by the position and number of sensors. The traditional development approach to hand gesture recognition systems follows a process of sensor pre-deployment, data collection, and model training, which is highly time-consuming and expensive to calculate how many sensors to place where, and the system has low secondary development flexibility. In this paper, we present a new development flow to assist in

Conventional motion tutorials rely mainly on a predefined motion and vision-based feedback that normally limits the application scenario and requires professional devices. In this paper, we propose VoLearn, a cross-modal system that provides operability for user-defined motion learning. The system supports the ability to import a desired motion from RGB video and animates the motion in a 3D virtual environment. We built an interface to operate on the input motion, such as controlling the speed,

A conventional motion exercises recognition system only tracks designated motion types, and it enables users cannot use a customized system according to personal needs. The virtual IMU data provides a new opportunity to reduce the cost of training datasets and flexibly design the activity recognition system using online resources. To better design a user-customized motion exercises recognition system using virtual IMU data, this paper proposes a virtual IMU sensor module with a spring-joint mode

An intelligent human activity recognition system is influenced to some extent by sensor placement. In this paper, the number, and placement positions, of wearable accelerometers have been investigated to determine their influence on a human activity recognition system. Given 17 possible human sensor placements, we developed a multi-stage and multi-swarm discrete particle swarm optimization algorithm to explore the optimal sensor combination for various required sensor amounts. Relevant experimen

Microbial fuel cells (MFCs) have been widely viewed as one of the most promising alternative renewable energy source. MFCs have attracted a large amount of interest in the past decade and much scientific effort has been dedicated to making this technology more efficient. MFCs are complex bioelectrochemical system that generates electrical energy by the catalytic reaction of organic substrates. The study of MFCs requires an interdisciplinary approach. Experimental method is time‐consuming and une

As one of the most natural user behaviors, walking has been widely focused on developing personal identification systems due to its unique biometric authentication features. Popular visual solutions are usually affected by various environmental conditions, and their redundant user information (e.g., body type and appearance) makes it more challenging for users to maintain privacy and security. This paper proposes a distance sensor–based gait identification system that uses only one-dimensional d

The diagnostic approach for knee osteoarthritis that draws on kinematic characteristics provides a solution other than imaging medicine. However, the gait-based kinematic analysis still requires a motion capture suit as a prerequisite to ensure a reliable calculation, which limits the daily screening of the end user. To further reduce the cost, in this paper we investigated a wearable inertial measurement unit (IMU)-based knee osteoarthritis classification system based on daily-use wearing IMU l

Following the conventional pipeline, the training dataset of a human activity recognition system relies on the detection of the significant signal variation regions. Such position-specific classifiers provide less flexibility for users to alter the sensor positions. In this paper, we proposed to employ the simulated sensor to generate the corresponding signal from human motion animation as the dataset. Visualizing the corresponding items from the real world, the user can determine the sensor’s p

The rise of the Internet of Things (IoT) has given birth to transformative and massively deployed computing applications that raise the significant issue of energy sources. It is impractical and irresponsible to rely on wires and batteries to power trillion-level devices. One promising prediction is that energy harvesting technologies will serve as alternative power sources for IoT devices. However, we might be losing this prophecy for lack of understanding of how novice developers comprehend en

Previous motor learning systems rely on a vision-based workflow both from feed-forward and feedback process, which limits the application requirement and scenario. In this demo, we presented a novel cross-modal motor learning system named VoLearn. The novice is able to interact with desired motion through a virtual 3D interface and obtain the audio feedback based on a personal smartphone. Both interactivity and user-accessibility of the designed system contribute to a wider range of applications

Human activity recognition systems combined with machine learning normally serve users based on the fixed sensor position. Uniform sensor position normally cannot satisfy the user’s demand according to different conditions. In this paper, we recognized the sensor position as an interface between the user and sensor system. We designed the optimization scheme to generate the best sensor position for activity recognition system. The user can indicate his/her preferred or disliked position and sens

Guiding users with limb exercise can assist in muscle training or physical recovery. However, traditional vision-based methods often require multiple camera angles to help users understand the motions and require them to be within the range of the screen. Therefore, we propose a non-visual system that can guide users with multiple-directional limb motions utilizing spatial audio, AudioMove , with commercial-off-the-shelf (COTS) devices (i.e., smartphones and earphones). The proposed system addre