Wireless sensing systems are required for continuous health monitoring and data collection. It allows for patient data collection in real time rather than through time-consuming and expensive hospital or lab visits. This technology employs wearable sensors, signal processing, and wireless data transfer to remotely monitor patients’ health. The research offers a novel approach to providing primary diagnostics remotely with a digital health system for monitoring pulmonary health status using a multimodal wireless sensor device. The technology uses a compact wearable with new integration of acoustics and biopotentials sensors to monitor cardiovascular and respiratory activity to provide comprehensive and fast health status monitoring. Furthermore, the small wearable sensor size may stick to human skin and record heart and lung activities to monitor respiratory health. This paper proposes a sensor data fusion method of lung sounds and cardiograms for potential real-time respiration pattern diagnostics, including respiratory episodes like low tidal volume and coughing. With a p-value of 0.003 for sound signals and 0.004 for electrocardiogram (ECG), preliminary tests demonstrated that it was possible to detect shallow breathing and coughing at a meaningful level.

Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm.

Electrooculography (EOG) is a technique for detecting electrical signals from the extra-ocular muscles. The EOG is a precise method for quantifying eye movements, including drowsiness-induced eye closure, and is also a promising technology for its potential use as a contributing mechanism for brain–computer interface applications. Despite the fact that EOG signals change as humans move their eyes, it is still difficult to monitor eye movement patterns in natural behaviors, such as everyday activity. Wearable convenience is essential for obtaining EOG signals while moving freely. This paper proposes the development and use of semi-dry electrodes with low impedance and excellent wearability, as well as a small, portable device with wireless communication capabilities, to increase the likelihood of use in real-life scenarios. The semi-dry electrode produced by the electrospinning technique had an impedance that was 3.5 times lower than that of the existing dry electrode and demonstrated low impedance drift even after long-term use. Furthermore, three steps of eye motion separation were performed using a signal obtained from the wearable device. It was confirmed that the classification of eye movements was at a meaningful level.

Gait signifies the walking pattern of an individual. It may be normal or abnormal, depending on the health condition of the individual. This paper considers the development of a gait sensor network system that uses a pair of wireless inertial measurement unit (IMU) sensors to monitor the gait cycle of a user. The sensor information is used for determining the normality of movement of the leg. The sensor system places the IMU sensors on one of the legs to extract the three-dimensional angular motions of the hip and knee joints while walking. The wearable sensor is custom-made at San Diego State University with wireless data transmission capability. The system enables the user to collect gait data at any site, including in a non-laboratory environment. The paper also presents the mathematical calculations to decompose movements experienced by a pair of IMUs into individual and relative three directional hip and knee joint motions. Further, a new approach of gait pattern classification based on the phase difference angles between hip and knee joints is presented. The experimental results show a potential application of the classification method in the areas of smart detection of abnormal gait patterns.

Spine movement is a daily activity that can indicate health status changes, including low back pain (LBP) problems. Repetitious and continuous movement on the spine and incorrect postures during daily functional activities may lead to the potential development and persistence of LBP problems. Therefore, monitoring of posture and movement is essential when designing LBP interventions. Typically, LBP diagnosis is facilitated by monitoring upper body posture and movement impairments, particularly during functional activities using body motion sensors. This study presents a fully wireless multi-sensor cluster system to monitor spine movements. The study suggests an attempt to develop a new method to monitor the lumbopelvic movements of interest selectively. In addition, the research employs a custom-designed robotic lumbar spine simulator to generate the ideal lumbopelvic posture and movements for reference sensor data. The mechanical motion templates provide an automated sensor pattern recognition mechanism for diagnosing the LBP.

Obtaining the mechanical properties of soft tissues is critical in many medical fields, such as regenerative medicine and surgical simulation training. Although various tissue-characterization methods have been developed, such as AFM, indentation, and elastography, there remain some limitations on their accuracy and validity for measuring small and fragile soft tissues. This paper presents a tensile testing technique to measure the mechanical properties of soft tissues directly and accurately. Tensile testing was chosen as the primary method because of its simple procedure and ability to derive mechanical properties without requiring many assumptions or complicated models. However, tensile testing on soft tissues presents challenges related to gripping the tissue sample without affecting its inherent properties, applying minuscule forces to the sample, and measuring the cross-section area and strain of the sample. To solve these issues, this study presents a sub-micro scale tensile testing system that uses a flexure mechanism and a novel 3D-printed sample holder for gripping the tissue samples. The system also measures tested samples' cross-section area and strain using two high-resolution cameras. The system was validated by testing standard materials and used to characterize the elastic modulus, yield stress, and yield strain of lung tissue slices from six different mice. The results from the validation tests showed a less than 2.5% error for elastic modulus values measured using the tensile tester. At the same time, results from the mice lung tissue measurements revealed qualitative findings that closely matched those seen in the literature and displayed low coefficient of variation values, demonstrating the high repeatability of the system.