keystone to creating temporally resolved therapeutics that can further reduce patient pains, prevent fatal events, and improve the wellbeing of individual life. One significant example with unmet needs for such technology focuses on peripheral artery disease (PAD), a family of disorder that causes stenosis or thrombus in the arteries/aorta of the limbs, compromises the physiological functions of the human extremities, and lead to symptoms including atypical leg pain and claudication. [1] Affecting the life quality of over 6.5 million Americans (5.8%, according to the 2000 Census population), [2] PAD harms the patient not only through its direct symptoms, but also by increasing the likelihood of myocardial infarction, ischemic stroke, and other cardiovascular diseases, which could further evolve into a prevalent factor of mobility impairment, mental issues, and mortality, especially in the elder. [3] Early warning signs correlated with PAD, which tend to be valuable in informing timely actions of preventive therapeutics before complications escalate, are, however, often neglected, or underappreciated, especially in low-resources settings. [4][5][6][7][8] Furthermore, the most commonly used indicator of PAD, ankle-brachial index (the ratio of the blood pressure measured at the ankle to the upper arm) has several major limitations, including: 1) long Continuous, real-time monitoring of biomarkers associated with local regions of the body can enhance both temporal and dimensional accuracy of proactive treatment to acute syndromes for critical illnesses, especially peripheral artery diseases. Conventional health monitors often face grand challenges in leveraging deep-tissue sensing capability with a safe and compatible biointerface.Optical-based noninvasive monitors may lack the ability to detect oximetry under subcutaneous fatty tissue due to the light scattering and absorption; implantables offer targeted sensing at depth, but may induce infection and inflammation. This report puts forward a wireless, wearable deep-tissue sensing patch by incorporating biocompatible microneedle waveguides at the sensing interface, to bypass the light extinction in epidermic and dermic tissue and enable the tracking of oximetry at muscular tissue. The sensing patch provides multiple physiological measurements at the sensing area, including tissue oximetry, pulse oximetry, heart pulsation, and respiratory activities with a wireless platform for uninterrupted data advertising and processing to enable real-time diagnostic analysis. The mechanical and thermal characterizations of the sensing patch with the microneedle waveguides validate the durable and safe operation at the skin interface. In vivo study with animal models of hindlimb ischemia demonstrates the high sensitivity and timely response of the sensing patch as muscle tissue hypoxia emerges.

Real-time monitoring of muscular oxygenation can enhance post-operative care of muscular wounds and peripheral artery disease. In article number 2200468, Wubin Bai and co-workers report a strategy by integrating microneedle waveguides with a wireless optoelectronic system in a thin, flexible construction to overcome the limitation of sensing depth and enable stable, continuous monitoring of muscular oxygenation without extensive implantation procedures.

Advanced technologies for muscle tracking provide easy access to identify and track muscle activity, often for the purposes of therapeutic interventions. The necessity of muscle trackers arises from the acute and chronic sources that disrupt neuromuscular control, resulting in an impaired ability to perform daily activities without assistance. In the context of human–machine interfaces, muscle trackers can serve as the “sensory” component, providing real‐time information to machines, such as exoskeletons and prosthetics, that can act upon such information for therapeutic and functional aid. Recently developed devices for muscle tracking rely on combinations of sensor modalities and algorithms to extract information from biosignals track muscle activity, or even extrapolate kinematic information, including gestures. However, a number of obstacles remain to be overcome to further facilitate the practical implementation of muscle‐tracking technologies, the most notable being real‐time analysis of biosignal data and extracting kinematic information from complex movements. This review attempts to cover the mechanisms behind various sensor modalities and algorithms commonly used for muscle tracking, as well as establish the current state of applications within the field. Given its multidisciplinary nature and ability to free users from rehabilitation constraints, the field of muscle tracking holds significant promise for future study.