Over the past several decades, near-infrared spectroscopy (NIRS) has become a popular research and clinical tool for non-invasively measuring the oxygenation of biological tissues, with particular emphasis on applications to the human brain. In most cases, NIRS studies are performed using continuous-wave NIRS (CW-NIRS), which can only provide information on relative changes in chromophore concentrations, such as oxygenated and deoxygenated hemoglobin, as well as estimates of tissue oxygen saturation. Another type of NIRS known as frequency-domain NIRS (FD-NIRS) has significant advantages: it can directly measure optical pathlength and thus quantify the scattering and absorption coefficients of sampled tissues and provide direct measurements of absolute chromophore concentrations. This review describes the current status of FD-NIRS technologies, their performance, their advantages, and their limitations as compared to other NIRS methods. Significant landmarks of technological progress include the development of both benchtop and portable/wearable FD-NIRS technologies, sensitive front-end photonic components, and high-frequency phase measurements. Clinical applications of FD-NIRS technologies are discussed to provide context on current applications and needed areas of improvement. The review concludes by providing a roadmap toward the next generation of fully wearable, low-cost FD-NIRS systems.

Machine Learning has potential applications across a wide spectrum of devices. However, current approaches for domain-specific accelerators have encountered difficulties in satisfying the most recent computational demands for machine learning applications. This work aims to create an adaptive acceleration framework for fNIRS motion artefact detection, which will be specifically designed for wearable devices. We evaluate the performance of the SVM classifier that has been implemented using SYCL on our fNIRS dataset across diverse devices and discuss the potential to accelerate more advanced motion artefact classifiers at the edge.

We present a remote-control, smartphone-based scanning system that can achieve a full-head 3D scan of an infant within 2 seconds. The scanned images can then be automatically aligned to generate a 3D head surface model.