Chronic widespread pain remains a complex and incompletely understood condition. To complement existing pain assessment strategies, this study explored the ecological validity of unobtrusively captured daily life physiological signals as indicators of pain. Therefore, we collected physiological data using a wearable wristband from 46 patients with chronic widespread pain for seven days. Linear mixed-effect models revealed several significant associations between physiological signals, such as mean heart rate and momentary pain intensity. However, making individual pain predictions with multivariate machine learning models did not add value. While this study underscores the potential of ambulatory physiology for pain assessment, future research should validate and expand upon these initial findings to further enhance pain management strategies.

The Otago Exercise Program (OEP) represents a crucial rehabilitation initiative tailored for older adults, aimed at enhancing balance and strength. Despite previous efforts utilizing wearable sensors for OEP recognition, existing studies have exhibited limitations in terms of accuracy and robustness. This study addresses these limitations by employing a single waist-mounted Inertial Measurement Unit (IMU) to recognize OEP exercises among community-dwelling older adults in their daily lives. A cohort of 36 older adults participated in laboratory settings, supplemented by an additional 7 older adults recruited for at-home assessments. The study proposes a Dual-Scale Multi-Stage Temporal Convolutional Network (DS-MS-TCN) designed for two-level sequence-to-sequence classification, incorporating them in one loss function. In the first stage, the model focuses on recognizing each repetition of the exercises (micro labels). Subsequent stages extend the recognition to encompass the complete range of exercises (macro labels). The DS-MS-TCN model surpasses existing state-of-the-art deep learning models, achieving f1-scores exceeding 80% and Intersection over Union (IoU) f1-scores surpassing 60% for all four exercises evaluated. Notably, the model outperforms the prior study utilizing the sliding window technique, eliminating the need for post-processing stages and window size tuning. To our knowledge, we are the first to present a novel perspective on enhancing Human Activity Recognition (HAR) systems through the recognition of each repetition of activities.

Unhealthy dietary habits are considered as the primary cause of various chronic diseases, including obesity and diabetes. The automatic food intake monitoring system has the potential to improve the quality of life (QoL) of people with diet-related diseases through dietary assessment. In this work, we propose a novel contactless radar-based approach for food intake monitoring. Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is employed to recognize fine-grained eating and drinking gestures. The fine-grained eating/drinking gesture contains a series of movements from raising the hand to the mouth until putting away the hand from the mouth. A 3D temporal convolutional network with self-attention (3D-TCN-Att) is developed to detect and segment eating and drinking gestures in meal sessions by processing the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this work collects data in continuous meal sessions (more realistic scenarios). We create a public dataset comprising 70 meal sessions (4,132 eating gestures and 893 drinking gestures) from 70 participants with a total duration of 1,155 minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are included in this dataset. To validate the performance of the proposed approach, seven-fold cross-validation method is applied. The 3D-TCN-Att model achieves a segmental F1-score of 0.896 and 0.868 for eating and drinking gestures, respectively. The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions.

Eating speed is an important indicator that has been widely investigated in nutritional studies. The relationship between eating speed and several intake-related problems such as obesity, diabetes, and oral health has received increased attention from researchers. However, existing studies mainly use self-reported questionnaires to obtain participants' eating speed, where they choose options from slow, medium, and fast. Such a non-quantitative method is highly subjective and coarse at the individual level. This study integrates two classical tasks in automated food intake monitoring domain: bite detection and eating episode detection, to advance eating speed measurement in near-free-living environments automatically and objectively. Specifically, a temporal convolutional network combined with a multi-head attention module (TCN-MHA) is developed to detect bites (including eating and drinking gestures) from IMU data. The predicted bite sequences are then clustered into eating episodes. Eating speed is calculated by using the time taken to finish the eating episode to divide the number of bites. To validate the proposed approach on eating speed measurement, a 7-fold cross validation is applied to the self-collected fine-annotated full-day-I (FD-I) dataset, and a holdout experiment is conducted on the full-day-II (FD-II) dataset. The two datasets are collected from 61 participants with a total duration of 513 h, which are publicly available. Experimental results show that the proposed approach achieves a mean absolute percentage error (MAPE) of 0.110 and 0.146 in the FD-I and FD-II datasets, respectively, showcasing the feasibility of automated eating speed measurement in near-free-living environments.

Food intake monitoring plays an important role in personal dietary systems. Numerous approaches have been proposed to automatically detect eating gestures using various sensors and machine learning. However, existing eating gesture detection approaches mainly focus on meal sessions. Such a task is still challenging in free-living environments due to longer monitoring duration and more non-feeding activities. This paper proposes a wearable Inertial Measurement Unit (IMU) based method to detect eating and drinking gestures in free-living environments. Two important factors that impede intake gesture detection in free-living environments are addressed: 1) how to handle IMU data from two hands, and 2) what is the impact of downsampling sensor data on performance. To integrate two-hand data, we propose a solution that combines hand mirroring and temporal concatenation techniques. The multi-stage temporal convolutional network (MS-TCN) is applied to effectively recognise intake gestures. A dataset contains 12 subjects with 67.5 h data is collected for validation. Moreover, IMU data with different sampling frequencies are processed to test performance. Validated by Leave-One-Subject-Out (LOSO) method, our approach (with 16 Hz sampling frequency) achieves a segmental F1-score of 0.826 and 0.893 for recognizing eating and drinking gestures, respectively. Results show that the proposed solution outperforms existing two-hand data combination approaches. Moreover, in our case, a higher sampling frequency does not always mean better performance.

Stress and mental health have become major concerns worldwide. Research has already extensively investigated physiological signals as quantitative and continuous markers of stress. In recent years, the focus of the field has shifted from the laboratory to the ambulatory environment. We provide an overview of physiological stress detection in laboratory settings with a focus on identifying physiological sensing priorities, including electrocardiogram, skin conductance, and electromyogram, and the most suitable machine learning techniques, of which the choice depends on the context of the application. Additionally, an overview is given of new challenges ahead to move toward the ambulant environment, including the influence of physical activity, lower signal quality due to motion artifacts, the lack of a stress reference, and the subject-dependent nature of the physiological stress response. Finally, several recommendations for future research are listed, focusing on large-scale, longitudinal trials across different population groups and just-in-time interventions to move toward disease prevention and interception.

Two 23 GHz low-noise amplifier (LNA) have been designed and implemented by 45 nm planar bulk-CMOS technology with high-Q above-IC inductors. In the designed LNAs, the structure of cascode amplifier with source inductive degeneration is used. All high-Q above-IC inductors have been implemented by thin-film wafer-level packaging (WLP) technology. The fabricated one-stage LNA has a good linearity where the input 1 dB compression point (IP-1dB) is -9.5 dBm and the input referred third-order intercept point (PIIP3) is +2.25 dBm. It is operated with a 1 V power supply drawing a current of only 3.6 mA. The fabricated one-stage LNA has demonstrated a 4 dB noise figure (NF) and a 7.1 dB gain at the peak gain frequency of 23 GHz. Moreover, the fabricated two-stage LNA has the IP-1dBof -16 dBm and the PIIP3of -4.2 dBm. It drains 9.3 mA from 1-V power supply. This two-stage LNA has demonstrated a 4.4 dB NF and a 11.6 dB gain at the peak gain frequency of 23.4 GHz. The fabricated one-stage LNA has the highest figure-of-merit (FOM). The experimental results have proved the suitability of 45 nm gate length planar bulk-CMOS devices for RF ICs above 20 GHz.

Power distribution and signal transmission are becoming key limiters for chip performance in nanometer era. These issues can be simultaneously addressed by designing transmission lines in power grids. The transmission lines are well suited for high quality intra-chip signal transmission at multi gigabit data rates. By having signal lines between the power grids, the VDD and GND lines in the grid can be exploited as return paths besides being used for regular power distribution. This approach also improves wiring density. In this paper, we rigorously analyze and discuss the design considerations for laying transmission lines in power grids. We also present design oriented modeling methods in 2D and 3D geometry. We show how the grid modeling complexity is simplified. We experimentally validate our results with fabricated test structures. We also show VDD lines in the grid act as good return path without external decoupling capacitors in our design. Further we discuss substrate effects and deduce guidelines for designing power grid transmission lines on a low resistive silicon substrate

As integrated circuit technology enters the nanometer era, global interconnects are becoming a bottleneck for overall chip performance. In this paper, we show that wafer-level package interconnects are an effective alternative to conventional on-chip global wires. These interconnects behave as LC transmission lines and can be exploited for their near speed of light transmission and low attenuation characteristics. We compare performance measures such as bandwidth, bandwidth density, latency, and power consumption of the package-level transmission lines with conventional on-chip global interconnects for different International Technology Roadmap for Semiconductors (ITRS) technology nodes. Based on these results, we show that package-level interconnects are well suited for power demanding low-latency applications. We also analyze different interconnect options such as memory buses, long inter tile interconnects, clock, and power distribution.

Future high-performance wireless communication applications such as wireless local area networks (WLANs) around 5 GHz require low-power and highly integrated transceiver solutions. The integration of the RF front end especially poses a great challenge in these applications, as traditional front-end implementations require a large number of external passive components. In this paper, we present the single-package integration of complete transceivers based on a thin-film multichip module (MCM) technology with integrated passives. The MCM substrate is a a common carrier onto which different ICs are mounted. passive components such as RF bandpass filters, inductors, capacitors, and resistors are directly integrated into the MCM substrate with the use of the multilayer structure of the MCM technology. The "system-on-a-package" approach is illustrated with a voltage-controlled oscillator for Digital European Cordless Telephone (DECT) applications and a 5-GHz WLAN front end. These examples indicate that this approach yields a compact low-power implementation of complete transceivers for high-performance wireless applications.