The most common method used to pick up biomedical signals is through metallic electrodes coupled to the input of high-gain, low-noise amplifiers. Unfortunately, electrodes, amongst other effects, introduce an undesired contact resistance and a contact potential. The contact potential needs to be rejected since it would otherwise cause the saturation of the input stage of the amplifiers, and this is almost always obtained by inserting a simple RC high-pass filter in the input signal path. The contact resistance needs to be estimated to ensure that it does not impair correct measurements. Methods exist for estimating the contact resistance by dynamically modifying the input network configuration, but because of the presence of the input RC filter, long transients are induced any time a switch occurs between different input configurations, so that the measurement time may become unacceptably long. In this paper, we propose a new topology for a DC removal network at the input of the differential signal amplifier that results in an AC filter whose time constant can be continuously changed by means of a control voltage. As such, we can speed up the recovery from transients by setting very short time constants (during the input resistance estimation process) while maintaining the ability to obtain very low cut-in frequencies by setting a much larger time constant during actual measurements. A prototype of the system was built and tested in order to demonstrate the advantage of the approach we propose in terms of reduced measurement time.

This study suggests using wearable activity trackers to identify mobility patterns in chronic complex patients (CCPs) and investigate their relation with the Barthel index (BI) to assess functional decline. CCPs are individuals who suffer from multiple, chronic health conditions that often lead to a progressive decline in their functional capacity. As a result, CCPs frequently require the use of healthcare and social resources, placing a significant burden on the healthcare system. Evaluating mobility patterns is critical for determining a CCP’s functional capacity and prognosis. To monitor the overall activity levels of CCPs, wearable activity trackers have been proposed. Utilizing the data gathered by the wearables, time series clustering with dynamic time warping (DTW) is employed to generate synchronized mobility patterns of the mean activity and coefficient of variation profiles. The research has revealed distinct patterns in individuals’ walking habits, including the time of day they walk, whether they walk continuously or intermittently, and their relation to BI. These findings could significantly enhance CCPs’ quality of care by providing a valuable tool for personalizing treatment and care plans.

Intraoperative Neurophysiological Monitoring is a set of monitoring techniques that reads electrical activity generated by the nervous system structures during surgeries. In non-trivial surgeries, neurophysiologists require a significant number of electrical signals to be picked up to check the effects of the surgeon’s actions in real time or to confirm that the correct nerves are selected. As a result, cabling the patient in the operating room can become cumbersome. The proposed WIONM module solves part of the problem by converting a good part of those cables into a wireless connection that is substantially transparent to the human operator and the existing medical instrumentation.

The human neck is highly vulnerable in motor vehicle crashes, and cervical spine response data are essential to improve injury prediction tools (e.g., crash test dummies, human body models). This feasibility study aimed to implement the use of pressure sensors in whole-body post-mortem human subject (PMHS) cervical spine intervertebral discs (IVDs) to confirm the feasibility and repeatability of cervical IVD pressure response to biomechanic research. Two fresh frozen whole-body PMHSs were instrumented with miniature pressure sensors (Model 060S, Precision Measurement Company, Ann Arbor, MI, USA) at three cervical IVD levels (C3/C4, C5/C6, and C7/T1) using minimally invasive surgical insertion techniques. Each PMHS underwent three quasistatic motion test trials, and each trial included multiple head/neck motions (i.e., gentle traction, flexion/extension, lateral bending, axial rotation, and forced tension/compression). Results showed marked pressure differences between both the cervical level assessed and the motion undertaken as well as successful intra-subject repeatability between the three motion trials. This study demonstrates that changes in cervical IVD pressure are associated with motion events of the cervical spine. Cervical IVD response data could be utilized to assess and supplement the characterization of the head/neck complex motion, and data could facilitate the continued improvement of injury prediction tools.

Background: Functional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases. A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic actions that stop the exacerbation of the disease. Objective: This study aimed to predict alterations in the clinical condition of patients with complex chronic diseases by predicting the Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence model and data collected through an internet of things mobility device. Methods: A 2-phase pilot prospective single-center observational study was designed. During both phases, patients were recruited, and a wearable activity tracker was allocated to gather physical activity data. Patients were categorized into class A (BI≤20; total dependence), class B (2060; moderate or mild dependence, or independent). Data preprocessing and machine learning techniques were used to analyze mobility data. A decision tree was used to achieve a robust and interpretable model. To assess the quality of the predictions, several metrics including the mean absolute error, median absolute error, and root mean squared error were considered. Statistical analysis was performed using SPSS and Python for the machine learning modeling. Results: Overall, 90 patients with complex chronic diseases were included: 50 during phase 1 (class A: n=10; class B: n=20; and class C: n=20) and 40 during phase 2 (class B: n=20 and class C: n=20). Most patients (n=85, 94%) had a caregiver. The mean value of the BI was 58.31 (SD 24.5). Concerning mobility aids, 60% (n=52) of patients required no aids, whereas the others required walkers (n=18, 20%), wheelchairs (n=15, 17%), canes (n=4, 7%), and crutches (n=1, 1%). Regarding clinical complexity, 85% (n=76) met patient with polypathology criteria with a mean of 2.7 (SD 1.25) categories, 69% (n=61) met the frailty criteria, and 21% (n=19) met the patients with complex chronic diseases criteria. The most characteristic symptoms were dyspnea (n=73, 82%), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78) of patients. The most important variables for predicting the BI were identified as the maximum step count during evening and morning periods and the absence of a mobility device. The model exhibited consistency in the median prediction error with a median absolute error close to 5 in the training, validation, and production-like test sets. The model accuracy for identifying the BI class was 91%, 88%, and 90% in the training, validation, and test sets, respectively. Conclusions: Using commercially available mobility recording devices makes it possible to identify different mobility patterns and relate them to functional capacity in patients with polypathology according to the BI without using clinical parameters. Trial Registration: