This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS heartbeat complexes data, locate heartbeats, and calculate a heart rate (HR) using the respiration signal. The accuracy of both methods will be evaluated by comparing located QRS complexes and inspiration maxima to reference positions. The findings of this study will ultimately contribute to the development of new, more accurate, and efficient methods for identifying heartbeats in respiratory signals, leading to better diagnosis and management of cardiovascular diseases, particularly during sleep where respiration monitoring is paramount to detect apnoea and other respiratory dysfunctions linked to a decreased life quality and known cause of cardiovascular diseases. Additionally, this work could potentially assist in determining the feasibility of using simple, no-contact wearable devices for obtaining simultaneous cardiology and respiratory data from a single device.

Utilising the open-source design of the Federica Prosthetic Hand, we introduce a redesigned print-in-place version that minimises assembly requirements without sacrificing functionality. The original Federica hand has a complicated, time-consuming assembly process. The print-in-place design removes the process of assembling the hand, making it easier for new users to use the design. The approach results in a 21% reduction in the bill of materials and a 21% reduction in weight. Though the 8 N force of the original Federica hand was not reached in this trial, the measured results show that under the same test conditions, the print-in-place hand can match the performance of the original. In a single-specimen analysis, the print-in-place hand produced a higher peak force in four of eight orientations. The redesigned prosthetic hand includes functional joints fully integrated into its structured design with a locking mechanism, eliminating the need for several metal bolts, and a revised dorsal finger contour, reducing the requirement for additional support structures during printing. As with the original hand, the print-in-place prosthetic is open source for anyone to access.

We present a dataset from a wearable sensor that monitors respiration, electrocardiography (one lead), body position/actigraphy, and skin temperature, suitable for sleep studies. We collected the dataset in parallel with standard in-laboratory polysomnography (PSG) and used it to validate ASPEN-derived metrics against clinical assessments. Data were collected from seven volunteers (mean age = 55.7 ± 12.5 years and body mass index (BMI) = 29.9 ± 3.78 kg/m²) for a total of 63 h during sleep studies at the Flinders University Sleep Lab. Our preliminary analysis shows broad agreement between standard metrics used to assess sleep quality measured from the wearable sensor and the resident (Compumedics) polysomnography system, such as respiratory rate, heart rate, and sleep posture. Therefore, we make this dataset of high-resolution ASPEN signals, along with synoptic participant data (age, gender, BMI), available to support the development and benchmarking of algorithms for wearable sleep monitoring.