This chapter introduces the anatomy and physiology of the respiratory system, and the reasons for measuring breathing events, particularly, using wearable sensors. Respiratory monitoring is vital including detection of sleep apnea and measurement of respiratory rate. The automatic detection of breathing patterns is equally important in other respiratory rehabilitation therapies, for example, magnetic resonance exams for respiratory triggered imaging, and synchronized functional electrical stimulation. In this context, the goal of many research groups is to create wearable devices able to monitor breathing activity continuously, under natural physiological conditions in different environments. Therefore, wearable sensors that have been used recently as well as the main signal processing methods for breathing analysis are discussed. The following sensor technologies are presented: acoustic, resistive, inductive, humidity, acceleration, pressure, electromyography, impedance, and infrared. New technologies open the door to future methods of noninvasive breathing analysis using wearable sensors associated with machine learning techniques for pattern detection.

Mechanomyography (MMG) registers lateral oscillations of contracting muscles. Functional electrical stimulation (FES) improves the rehabilitation of paraplegic subjects and can be used in neuroprosthesis control. During FES application, muscular contraction responses may vary, possibly due to fatigue or adaptation of nerve cells face to electrical stimuli. This study measured the differences in MMG RMS and median frequency (MF) features between healthy (HV) and spinal cord injury (SCI) volunteers. Ten HV and three SCI participated in the research. FES waveform consisted of a monophasic square wave, 1kHz pulse frequency, 100us active pulse period and 3ms active burst period with burst frequency of 70Hz. For each stimulation series, three analysis windows were inspected. RMS and MF variations were inversely related. The obtained results may help to create new strategies of muscular closed-loop control.

Introduction: Functional Electrical Stimulation (FES) is a technique used in the restoration and generation of movements performed by subjects with neuromuscular disorders such as spinal cord injury (SCI). The purpose of this article is to outline the state of the art and perspectives of the use of FES in artificial motor control of the upper limbs in paretic or plegic people. Methods The databases used in papers selection were Google Scholar and Capes’ Portals as well as proceedings of the Annual Conference of the International Functional Electrical Stimulation Society (IFESS). Results Approximately 85% of the reviewed studies showed FES profile with pulse duration ranging from 1 to 300 μs and modulating (burst) frequency between 10 and 40 Hz. Regarding the type of electrodes, 88% of the studies employed transcutaneous electrodes. Conclusion We concluded that FES with closed-loop feedback and feedforward are the most used and most viable systems for upper limbs motor control, because they perform self-corrections slowing neuromuscular adaptation, allowing different planes and more range of movement and sensory-motor integration. One of the difficulties found in neuroprosthesis systems are electrical wires attached to the user, becoming uninteresting in relation to aesthetics and break. The future perspectives lead to a trend to miniaturization of the stimulation equipment and the availability of wireless networks, which allow the attachment of modules to other components without physical contact, and will become more attractive for daily use

(1) Background: The Modified Ashworth Scale (MAS) is commonly used clinically to evaluate spasticity, but its qualitative nature introduces subjectivity. We propose a novel metric scale to quantitatively measure spasticity using mechanomyography (MMG) to mitigate these subjective effects. (2) Methods: The flexor and extensor muscles of knee and elbow joints were assessed with the Modified Ashworth Scale (MAS) during the acquisition of mechanomyography (MMG) data. The median absolute amplitude of the MMG signals was utilized as a key descriptor. An algorithm was developed to normalize the MMG signals to a universal gravitational (G) acceleration scale, aligning them with the limits and range of MAS. (3) Results: We evaluated 34 lower and upper limbs from 22 volunteers (average age 39.91 ± 13.77 years) of both genders. Polynomial regression provided the best fit (R² = 0.987), with negligible differences (mean of 0.001 G) between the MAS and MMG. We established three numerical sets for the median, minimum, and maximum MMG(G) values corresponding to each MAS range, ensuring consistent alignment of the Modified Ashworth levels with our proposed scale. (4) Conclusions: Muscle spasticity can now be quantitatively and semi-automatically evaluated using our algorithm and instrumentation, enhancing the objectivity and reliability of spasticity assessments.