A U - Net Deep Learning Model for Infant Heart Rate Estimation from Ballistography.

Ballistography(BSG) is a non-intrusive and low- cost alternative to electrocardiography (ECG) for heart rate (HR) monitoring in infants. Due to the inter-patient variance and susceptibility to noise, heartbeat detection in the BSG waveform remains a challenge. The aim of this study was to estimate HR from a bed-based pressure mat BSG signal using a deep learning approach. We trained a U-Net deep neural network through supervised learning by deriving ground truth as the location of the heartbeats from simultaneously recorded ECG signals after peak matching. For improved generalization, we modified an existing U - Net to include an IC-layer. A predictive performance of 80% was achieved using the U-Net without the IC-layer. The inclusion of the IC-layer, while improving the generalization ability of the model to detect heartbeats, did not improve the HR estimation performance.

Monitoring the respiratory rate of preterm infants using an ultrathin film sensor embedded in the bedding: a comparative feasibility study.

OBJECTIVE: To determine the feasibility of unobtrusively monitoring the respiratory rate (RR) in preterm infants by using a film-like pressure sensor placed between the mattress and the bedding. APPROACH: The RR was simultaneously measured by processing the chest impedance (CI) and the ballistographic (BSG) signal acquired from the pressure sensor in 10 preterm infants of varying body weight. Nearly 27 h of data were analyzed from these infants while in different body positions including both spontaneously breathing infants and those receiving non-invasive respiratory support. MAIN RESULTS: The RR acquired from the BSG signal (RR-BSG) was significantly correlated (r = 0.74) to the RR derived from the CI (RR-CI) with narrow 95% limits of agreement (10 breaths min-1). A subanalysis of epochs most and least affected by infant movement yielded comparable results. SIGNIFICANCE: Irrespective of body weight or infant position, unobtrusively monitoring the RR of preterm infants is feasible using film-like pressure sensors.

A Ballistographic Approach for Continuous and Non-Obtrusive Monitoring of Movement in Neonates.

Continuously monitoring body movement in preterm infants can have important clinical applications since changes in movement-patterns can be a significant marker for clinical deteriorations including the onset of sepsis, seizures, and apneas. This paper proposes a system and method to monitor body movement of preterm infants in a clinical environment using ballistography. The ballistographic signal (BSG) is acquired using a thin and a film-like sensor that is placed underneath an infant. Manual annotations based on video-recordings served as a reference standard for identifying movement. We investigated the performance of multiple features, constructed from the BSG waveform, to discriminate movement from no movement based on data acquired from 10 preterm infants. Since routine cardiorespiratory monitoring is prone to movement artifacts, we also compared the application of these features on the simultaneously acquired cardiorespiratory waveforms, i.e., the electrocardiogram, the chest impedance, and the photoplethysmogram. The BSG-based-features consistently outperformed those based on the routinely acquired cardiorespiratory waveforms. The best performing BSG-based feature-the signal instability index-had a mean (standard deviation) effect size of 0.90 (0.06), as measured by the area under the receiver operating curve. The proposed system for monitoring body movement is robust to noise, non-obtrusive, and has high performance in clinical settings.

Improving posture and sitting behavior through tactile and visual feedback in a sedentary environment.

This paper presents the concept Backtive: an interactive office chair with an accompanying mobile application to provide both tactile and visual feedback for the user to correct his posture and make him more aware of the sitting behavior throughout the day. The concept aims to make people unintentionally and more voluntarily aware of their sitting behavior while not interrupting their daily tasks. The concept is developed through a user centered design approach and by building a prototype to test feasibility as well as evaluating the concept. An evaluation with four persons received positive results with regard to credibility and expectancy of Backtive for posture improvement. Participants also expect that Backtive would be easy in use and are likely to integrate the system in their daily activities.