Rationale: Ventilator-induced diaphragm dysfunction is a significant contributor to weaning difficulty in ventilated critically ill patients. It has been hypothesized that electrically pacing the diaphragm during mechanical ventilation could reduce diaphragm dysfunction. Objectives: We tested a novel, central line catheter–based, transvenous phrenic nerve pacing therapy for protecting the diaphragm in sedated and ventilated pigs. Methods: Eighteen Yorkshire pigs were studied. Six pigs were sedated and mechanically ventilated for 2.5 days with pacing on alternate breaths at intensities that reduced the ventilator pressure–time product by 20–30%. Six matched subjects were similarly sedated and ventilated but were not paced. Six pigs served as never-ventilated, never-paced control animals. Measurements and Main Results: Cumulative duration of pacing therapy ranged from 19.7 to 35.7 hours. Diaphragm thickness assessed by ultrasound and normalized to initial value showed a significant decline in ventilated–not paced but not in ventilated-paced subjects (0.84 [interquartile range (IQR), 0.78–0.89] vs. 1.10 [IQR, 1.02–1.24]; P = 0.001). Compared with control animals (24.6 μm2/kg; IQR, 21.6–26.0), median myofiber cross-sectional areas normalized to weight and sarcomere length were significantly smaller in the ventilated–not paced (17.9 μm2/kg; IQR, 15.3–23.7; P = 0.005) but not in the ventilated-paced group (24.9 μm2/kg; IQR, 16.6–27.3; P = 0.351). After 60 hours of mechanical ventilation all six ventilated-paced subjects tolerated 8 minutes of intense phrenic stimulation, whereas three of six ventilated–not paced subjects did not (P = 0.055). There was a nonsignificant decrease in diaphragm tetanic force production over the experiment in the ventilated-paced and ventilated–not paced groups. Conclusions: These results suggest that early transvenous phrenic nerve pacing may mitigate ventilator-induced diaphragm dysfunction.

The vertical ground reaction force (vGRF) and its passive and active peaks are important gait parameters and of great relevance for musculoskeletal injury analysis and prevention, the detection of gait abnormities, and the evaluation of lower-extremity prostheses. Most currently available methods to estimate the vGRF require a force plate. However, in real-world scenarios, gait monitoring would not be limited to a laboratory setting. This paper reports a novel solution using machine learning algorithms to estimate the vGRF and the timing and magnitude of its peaks from data collected by a single inertial measurement unit (IMU) on one of the lower limb locations. Nine volunteers participated in this study, walking on a force plate-instrumented treadmill at various speeds. Four IMUs were worn on the foot, shank, distal thigh, and proximal thigh, respectively. A random forest model was employed to estimate the vGRF from data collected by each of the IMUs. We evaluated the performance of the models against the gold standard measurement of the vGRF generated by the treadmill. The developed model achieved a high accuracy with a correlation coefficient, root mean square error, and normalized root mean square error of 1.00, 0.02 body weight (BW), and 1.7% in intra-participant testing, and 0.97, 0.10 BW, and 7.15% in inter-participant testing, respectively, for the shank location. The difference between the reference and estimated passive force peak values was 0.02 BW and 0.14 BW with a delay of −0.14% and 0.57% of stance duration for the intra- and inter-participant testing, respectively; the difference between the reference and estimated active force peak values was 0.02 BW and 0.08 BW with a delay of 0.45% and 1.66% of stance duration for the intra- and inter-participant evaluation, respectively. We concluded that vertical ground reaction force can be estimated using only a single IMU via machine learning algorithms. This research sheds light on the development of a portable wearable gait monitoring system reporting the real-time vGRF in real-life scenarios.

Mobile health technology and activity tracking with wearable sensors enable continuous unobtrusive monitoring of movement and biophysical parameters. Advancements in clothing‐based wearable devices have employed textiles as transmission lines, communication hubs, and various sensing modalities; this area of research is moving towards complete integration of circuitry into textile components. A current limitation for motion tracking is the need for communication protocols demanding physical connection of textile with rigid devices, or vector network analyzers (VNA) with limited portability and lower sampling rates. Inductor–capacitor (LC) circuits are ideal candidates as textile sensors can be easily implemented with textile components and allow wireless communication. In this paper, the authors report a smart garment that can sense movement and wirelessly transmit data in real time. The garment features a passive LC sensor circuit constructed of electrified textile elements that sense strain and communicate through inductive coupling. A portable, lightweight reader (fReader) is developed for achieving a faster sampling rate than a downsized VNA to track body movement, and for wirelessly reading sensor information suitable for deployment with a smartphone. The smart garment–fReader system monitors human movement in real‐time and exemplifies the potential of textile‐based electronics moving forward.

The development of flexible strain sensors over the past decade has focused on accessing high strain percentages and high sensitivity (i.e., gauge factors). Strain sensors that employ capacitance as the electrical signal to correlate to strain are typically restricted in sensitivity because of the Poisson effect. By employing auxetic structures, the limits of sensitivity for capacitive sensors have been exceeded, which has improved the competitiveness of this modality of sensing. In this work, the first employment of helical auxetic yarns as capacitive sensors is presented. It is found that the response of the helical auxetic yarn capacitive sensors (termed as HACS) is dependent on the two main fabrication variables—the ratio of diameters and the helical wrapping length. Depending on these variables, sensors that respond to strain with increasing or decreasing capacitance values can be obtained. A greater auxetic character results in larger sensitivities accessible at smaller strains—a characteristic that is not commonly found when accessing high gauge factors. In addition, the highest sensitivity for auxetic capacitive sensors reported thus far is obtained. A mechanism of sensor response that explains both the variable capacitance response and the high gauge factors obtained experimentally is proposed.

Textile sensors transform our everyday clothing into a means to track movement and biosignals in a completely unobtrusive way. One major hindrance to the adoption of “smart” clothing is the difficulty encountered with connections and space when scaling up the number of sensors. There is a lack of research addressing a key limitation in wearable electronics: Connections between rigid and textile elements are often unreliable, and they require interfacing sensors in a way incompatible with textile mass production methods. We introduce a prototype garment, compact readout circuit, and algorithm to measure localized strain along multiple regions of a fiber. We use a helical auxetic yarn sensor with tunable sensitivity along its length to selectively respond to strain signals. We demonstrate distributed sensing in clothing, monitoring arm joint angles from a single continuous fiber. Compared to optical motion capture, we achieve around five degrees error in reconstructing shoulder, elbow, and wrist joint angles.

Objective: Handheld dynamometers are common tools for assessing/monitoring muscular strength and endurance. Health/fitness Bluetooth load sensors may provide a cost-effective alternative; however, research is needed to evaluate the validity and reliability of such devices. This study assessed the validity and reliability of two commercially available Bluetooth load sensors (Activ5 by Activbody and Progressor by Tindeq). Methods: Four tests were conducted on each device: stepped loading, stress relaxation, simulated exercise, and hysteresis. Each test type was repeated three times using the Instron ElectroPuls mechanical testing device (a gold-standard system). Test–retest reliability was assessed through intraclass correlations. Agreement with the gold standard was assessed with Pearson’s correlation, interclass correlation, and Lin’s concordance correlation. Results: The Activ5 and Progressor had excellent test–retest reliability across all four tests (ICC(3,1) ≥ 0.999, all p ≤ 0.001). Agreement with the gold standard was excellent for both the Activ5 (ρ ≥ 0.998, ICC(3,1) ≥ 0.971, ρ_c ≥ 0.971, all p’s ≤ 0.001) and Progressor (ρ ≥ 0.999, ICC(3,1) ≥ 0.999, ρ_c ≥ 0.999, all p’s ≤ 0.001). Measurement error increased for both devices as applied load increased. Conclusion: Excellent test–retest reliability was found, suggesting that both devices can be used in a clinical setting to measure patient progress over time; however, the Activ5 consistently had poorer agreement with the gold standard (particularly at higher loads).