Improved sensor selection method during movement for breathing rate estimation with unobtrusive pressure sensor arrays

Use of pressure sensor arrays as an unobtrusive way of monitoring physiological characteristics of human beings is a growing field of research. In such pressure sensor arrays, monitoring respiratory signal during movement is a challenge that researchers and engineers are currently faced with. This paper presents an improved method to reliably find breathing rate during movement using unobtrusive bed-based pressure-sensor array. We use spectral flatness ratio and signal variance to select the most powerful sensors for which breathing dominates the signals. We also apply movement detection prior to breathing rate estimation based on a recently developed movement detection algorithm in order to minimize movement effects. The proposed method was applied to nocturnal data collected from a male subject and a female subject, and performance is analyzed. Our results show that this scheme can lead to a higher reliability of estimate even when more than 50% of data are corrupted with movement

Automatic apnea-hypopnea events detection using an alternative sensor

According to the American Academy of Sleep Medicine (AASM) 2007 scoring manual, the sum of dual respiratory inductive plethysmography signals, RIP-sum, can be used as an alternative signal instead of airflow signal to detect sleep apnea events. In this study, a new method is proposed to detect apnea-hypopnea events using the RIP-sum signal. An event-based metric is used to evaluate the results of the proposed method. The results showed that the RIP-sum signal could be a reliable alternative signal to detect sleep apnea-hypopnea events using the proposed method

Movement detection with adaptive window length for unobtrusive bed-based pressure-sensor array

Use of automated and unobtrusive sensors for physiological monitoring has become popular nowadays, since no devices need to be worn by individuals and it does not require any user interaction. However, when bodily movements occur, movement artifacts are introduced which can interfere with the breathing signal. This paper proposes a method to automatically identify movement onset and offset times when using an unobtrusive bed-based pressure-sensor array. This work makes use of a previously developed method for movement detection based on control levels. The novel contribution of this paper is employing an adaptive window length to calculate a moving average and a moving variance, by measuring the distance between two consecutive peaks in the signal which relates to consecutive movements. We also impose a threshold based on the weight and height of an individual to flag true movements and discard false ones. The proposed method is applicable for different postures and breath patterns of the bed occupant. Our experimental results show that the proposed scheme can lead to an average movement detection offset as low as 1.32 second, with no false-positive events and low false-negatives, and it provides significant improvements compared to a previous method

Breathing signal combining for respiration rate estimation in smart beds

One of the non-invasive ways to measure respiratory effort is in-bed pressure sensor arrays. Based on the area of the bed and the sensor array covered by a patient's body, some sensors may not include significant respiratory effort components or may have low signal to noise ratios. When combining signals from the different sensors, this can produce a low quality output signal. Signal combiners can overcome this problem. This paper describes two different methods of signal combining to achieve a good estimation of the respiratory rate and the respiratory signal itself. To assess the performance, a participant was asked to lay on the bed in supine position while having normal breathing. Our results indicate that both methods can perform very satisfactorily when compared to a gold standard signal, and that they can outperform some previously published methods

Literacy toy for enhancement phonological awareness: A longitudinal study

In this report it is presented the results of a longitudinal pre-experimental study, it was realized a technological intervention to stimulate the phonological awareness through a tangible reading toy based on the RFID technology, consisting of a teddy bear and 30 letters in 3D from the Spanish alphabet. This study started with a sample of 200 children, from them, there were selected 17 children aged between 6 and 7 years (M age = 6.47, SD =.51) with a phonological disorder from an educative institution. The procedure consisted of obtaining pre-test and post-test values with the Evaluation of Phonological Awareness (PECFO). Sampling inclusion criteria considered children presenting problems of phonemes’ recognition and its relationship with graphemes. During 30 weeks it was realized an intervention with the technological toy and at the end of the sessions, it was applied the post-test. Results of phonological awareness showed statically significant differences among the pre (M = 12.88, SD = 3.53) and post-test (M = 17.17, SD = 2.96) this contributes to the empirical evidence of the intervened group improvement in this cognitive function t(16) = −3.67, p =.002. From this research it is projected proposing technological innovations contributing in the treatment of children’s cognitive difficulties. © 2020, The Author(s)