Wearable SiPM-Based NIRS Interface Integrated With Pulsed Laser Source

Integrated Real-Time CMOS Luminescence Sensing and Impedance Spectroscopy in Droplet Microfluidics

High-throughput biosensor screening and optimization are critical for health and environmental monitoring applications to ensure rapid and accurate detection of biological and chemical targets. Traditional biosensor design and optimization methods involve labor-intensive processes, such as manual pipetting of large sample volumes, making them low throughput and inefficient for large-scale library screenings under various environmental and chemical conditions. We address these challenges by introducing a modular droplet microfluidic system embedded with custom CMOS integrated circuits (ICs) for impedance spectroscopy and bioluminescence detection. Fabricated in a 65 nm process, our CMOS ICs enable efficient droplet detection and analysis. We demonstrate successful sensing of luciferase enzyme-substrate reactions in nL-volume droplets. The impedance spectroscopy chip detects 4 nL droplets at 67 mm/s with a 45 pA resolution, while the luminescence detector senses optical signals from 38 nL droplets with a 6.7 nA/count resolution. We show real-time concurrent use of both detection methods within our hybrid platform for cross-validation. This system greatly advances conventional biosensor testing by increasing flexibility, scalability, and cost-efficiency.

Time domain optical imaging device based on a commercial time-to-digital converter

Time-domain diffuse optical imaging is a noninvasive technique that uses pulsed near-infrared light as the interrogation source to produce quantitative images displaying the variation in blood volume and oxygenation in the human brain. Measuring the times of flights of photons provides information on the photon pathlengths in tissue, which enables absolute concentrations of the oxygenated and deoxygenated forms of hemoglobin to be estimated. Recent advances in silicon electronics have enabled the development of time-domain systems, which are lightweight and low cost, potentially enabling the imaging technique to be applied to a far greater cohort of subjects in a variety of environments. While such technology usually depends on customized circuits, in this article, we present a system assembled from commercially available components, including a low-cost time-to-digital converter and a silicon photomultiplier detector. The system is able to generate histograms of photon flight times at a rate of 81-90 kS/s and with a sampled bin width of 54 ps. The linearity and performance of the system are presented, and its potential as the basis for a modular multi-detector imaging system is explored.

Lawrence K. Incorporating early and late-arriving photons to improve the reconstruction of cerebral hemodynamic responses acquired by time-resolved near-infrared spectroscopy

Significance: Despite its advantages in terms of safety, low cost, and portability, functional near-infrared spectroscopy applications can be challenging due to substantial signal contamination from hemodynamics in the extracerebral layer (ECL). Time-resolved near-infrared spectroscopy (tr NIRS) can improve sensitivity to brain activity but contamination from the ECL remains an issue. This study demonstrates how brain signal isolation can be further improved by applying regression analysis to tr data acquired at a single source–detector distance.

Aim: To investigate if regression analysis can be applied to single-channel trNIRS data to further isolate the brain and reduce signal contamination from the ECL.

Approach: Appropriate regressors for trNIRS were selected based on simulations, and performance was evaluated by applying the regression technique to oxygenation responses recording during hypercapnia and functional activation.

Results: Compared to current methods of enhancing depth sensitivity for trNIRS (i.e., higher statistical moments and late gates), incorporating regression analysis using a signal sensitive to the ECL significantly improved the extraction of cerebral oxygenation signals. In addition, this study demonstrated that regression could be applied to trNIRS data from a single detector using the early arriving photons to capture hemodynamic changes in the ECL.

Conclusion: Applying regression analysis to trNIRS metrics with different depth sensitivities improves the characterization of cerebral oxygenation signals.

Review and Challenges of Technologies for Real-Time Human Behavior Monitoring

A person's behavior significantly influences their health and well-being. It also contributes to the social environment in which humans interact, with cascading impacts to the health and behaviors of others. During social interactions, our understanding and awareness of vital nonverbal messages expressing beliefs, emotions, and intentions can be obstructed by a variety of factors including greatly flawed self-awareness. For these reasons, human behavior is a very important topic to study using the most advanced technology. Moreover, technology offers a breakthrough opportunity to improve people's social awareness and self-awareness through machine-enhanced recognition and interpretation of human behaviors. This paper reviews (1) the social psychology theories that have established the framework to study human behaviors and their manifestations during social interactions and (2) the technologies that have contributed to the monitoring of human behaviors. State-of-the-art in sensors, signal features, and computational models are categorized, summarized, and evaluated from a comprehensive transdisciplinary perspective. This review focuses on assessing technologies most suitable for real-time monitoring while highlighting their challenges and opportunities in near-future applications. Although social behavior monitoring has been highly reported in psychology and engineering literature, this paper uniquely aims to serve as a disciplinary convergence bridge and a guide for engineers capable of bringing new technologies to bear against the current challenges in real-time human behavior monitoring.

Probe-hosted large area silicon photomultiplier and high-throughput timing electronics for enhanced performance time-domain functional near-infrared spectroscopy

Two main bottlenecks prevent time-domain diffuse optics instruments to reach their maximum performances, namely the limited light harvesting capability of the detection chain and the bounded data throughput of the timing electronics. In this work, for the first time to our knowledge, we overcome both those limitations using a probe-hosted large area silicon photomultiplier detector coupled to high-throughput timing electronics. The system performances were assessed based on international protocols for diffuse optical imagers showing better figures with respect to a state-of-the-art device. As a first step towards applications, proof-of-principle in-vivo brain activation measurements demonstrated superior signal-to-noise ratio as compared to current technologies.