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1. MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing

   https://doi.org/10.3390/s21010243  

   Wang, Lening ; Du, Pang ; Jin, Ran ( January 2021 , Sensors)

   Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ process variables from different sensor modalities to reflect the process variations in real-time. However, in practice, we usually do not have enough budget to equip too many sensors in each manufacturing process due to the cost consideration. Moreover, it is also important to better interpret the relationship between the sensing modalities and the quality variables based on the model. Therefore, it is necessary to model the quality-process relationship by selecting the most relevant sensor modalities with the specific quality measurement from the multi-modal sensing system in smart manufacturing. In this research, we adopted the concept of best subset variable selection and proposed a new model called Multi-mOdal beSt Subset modeling (MOSS). The proposed MOSS can effectively select the important sensor modalities and improve the modeling accuracy in quality-process modeling via functional norms that characterize the overall effects of individual modalities. The significance of sensor modalities can be used to determine the sensor placement strategy in smart manufacturing.more »Moreover, the selected modalities can better interpret the quality-process model by identifying the most correlated root cause of quality variations. The merits of the proposed model are illustrated by both simulations and a real case study in an additive manufacturing (i.e., fused deposition modeling) process.« less
2. BioFace-3D: continuous 3d facial reconstruction through lightweight single-ear biosensors

   https://doi.org/10.1145/3447993.3483252  

   Wu, Yi ; Kakaraparthi, Vimal ; Li, Zhuohang ; Pham, Tien ; Liu, Jian ; Nguyen, Phuc ( October 2021 , Proceedings of the 27th Annual International Conference on Mobile Computing and Networking (MobiCom '21))

   Over the last decade, facial landmark tracking and 3D reconstruction have gained considerable attention due to their numerous applications such as human-computer interactions, facial expression analysis, and emotion recognition, etc. Traditional approaches require users to be confined to a particular location and face a camera under constrained recording conditions (e.g., without occlusions and under good lighting conditions). This highly restricted setting prevents them from being deployed in many application scenarios involving human motions. In this paper, we propose the first single-earpiece lightweight biosensing system, BioFace-3D, that can unobtrusively, continuously, and reliably sense the entire facial movements, track 2D facial landmarks, and further render 3D facial animations. Our single-earpiece biosensing system takes advantage of the cross-modal transfer learning model to transfer the knowledge embodied in a high-grade visual facial landmark detection model to the low-grade biosignal domain. After training, our BioFace-3D can directly perform continuous 3D facial reconstruction from the biosignals, without any visual input. Without requiring a camera positioned in front of the user, this paradigm shift from visual sensing to biosensing would introduce new opportunities in many emerging mobile and IoT applications. Extensive experiments involving 16 participants under various settings demonstrate that BioFace-3D can accurately track 53 major facialmore »landmarks with only 1.85 mm average error and 3.38\% normalized mean error, which is comparable with most state-of-the-art camera-based solutions. The rendered 3D facial animations, which are in consistency with the real human facial movements, also validate the system's capability in continuous 3D facial reconstruction.« less
3. Experimental Exploration of Objective Human Pain Assessment Using Multimodal Sensing Signals

   https://doi.org/10.3389/fnins.2022.831627  

   Lin, Yingzi ; Xiao, Yan ; Wang, Li ; Guo, Yikang ; Zhu, Wenchao ; Dalip, Biren ; Kamarthi, Sagar ; Schreiber, Kristin L. ; Edwards, Robert R. ; Urman, Richard D. ( February 2022 , Frontiers in Neuroscience)

   Optimization of pain assessment and treatment is an active area of research in healthcare. The purpose of this research is to create an objective pain intensity estimation system based on multimodal sensing signals through experimental studies. Twenty eight healthy subjects were recruited at Northeastern University. Nine physiological modalities were utilized in this research, namely facial expressions (FE), electroencephalography (EEG), eye movement (EM), skin conductance (SC), and blood volume pulse (BVP), electromyography (EMG), respiration rate (RR), skin temperature (ST), blood pressure (BP). Statistical analysis and machine learning algorithms were deployed to analyze the physiological data. FE, EEG, SC, BVP, and BP proved to be able to detect different pain states from healthy subjects. Multi-modalities proved to be promising in detecting different levels of painful states. A decision-level multi-modal fusion also proved to be efficient and accurate in classifying painful states.
4. An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN

   https://doi.org/10.1145/3563949  

   Zargari, Amir Hosein ; Aqajari, Seyed Amir ; Khodabandeh, Hadi ; Rahmani, Amir M. ; Kurdahi, Fadi ( January 2022 , ACM Transactions on Computing for Healthcare)

   A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e.g., heart rate variability, blood pressure, and respiration rate. PPG signals can easily be collected continuously and remotely using portable wearable devices. However, these measuring devices are vulnerable to motion artifacts caused by daily life activities. The most common ways to eliminate motion artifacts use extra accelerometer sensors, which suffer from two limitations: i) high power consumption and ii) the need to integrate an accelerometer sensor in a wearable device (which is not required in certain wearables). This paper proposes a low-power non-accelerometer-based PPG motion artifacts removal method outperforming the accuracy of the existing methods. We use Cycle Generative Adversarial Network to reconstruct clean PPG signals from noisy PPG signals. Our novel machine-learning-based technique achieves 9.5 times improvement in motion artifact removal compared to the state-of-the-art without using extra sensors such as an accelerometer, which leads to 45% improvement in energy efficiency.
5. Activity recognition in manufacturing: The roles of motion capture and sEMG+inertial wearables in detecting fine vs. gross motion

   https://doi.org/10.1109/ICRA.2019.8793954  

   Kubota, Alyssa ; Iqbal, Tariq ; Shah, Julie A. ; Riek, Laurel D. ( January 2019 , 2019 International Conference on Robotics and Automation (ICRA))

   In safety-critical environments, robots need to reliably recognize human activity to be effective and trust-worthy partners. Since most human activity recognition (HAR) approaches rely on unimodal sensor data (e.g. motion capture or wearable sensors), it is unclear how the relationship between the sensor modality and motion granularity (e.g. gross or fine) of the activities impacts classification accuracy. To our knowledge, we are the first to investigate the efficacy of using motion capture as compared to wearable sensor data for recognizing human motion in manufacturing settings. We introduce the UCSD-MIT Human Motion dataset, composed of two assembly tasks that entail either gross or fine-grained motion. For both tasks, we compared the accuracy of a Vicon motion capture system to a Myo armband using three widely used HAR algorithms. We found that motion capture yielded higher accuracy than the wearable sensor for gross motion recognition (up to 36.95%), while the wearable sensor yielded higher accuracy for fine-grained motion (up to 28.06%). These results suggest that these sensor modalities are complementary, and that robots may benefit from systems that utilize multiple modalities to simultaneously, but independently, detect gross and fine-grained motion. Our findings will help guide researchers in numerous fields of robotics includingmore »learning from demonstration and grasping to effectively choose sensor modalities that are most suitable for their applications.« less