A wearable system for respiratory signal filtering based on activity: a preliminary validation

Angelucci, Alessandra; Camuncoli, Federica; Galli, Manuela; Aliverti, Andréa

doi:10.1109/star53492.2022.9860001

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…The current work presents a new approach for the remote monitoring of respiratory kinematics via smartphone-embedded sensors. The state of the art already reports similar methodologies; however, most of them rely on the adoption of wireless communication with standalone transducers and wearable devices [26,27], or they are extremely focused on the estimation of RR only [28], without a deep investigation into the effect of different device positions on the chest-abdomen. In fact, although smartphone-embedded IMUs are adopted in a few cases for respiratory kinematics recording, they are used to monitor a limited set of positions, neglecting the chest-abdomen kinematic difference [28,29].…”

Section: Discussionmentioning

confidence: 99%

“…In the literature, accelerometer measurements for respiratory kinematics at the chest and abdomen level have already been reported [23][24][25], with successful results in different contexts. From the literature, it appears that mostly wearable systems with standalone sensors and wireless communication have been used [26,27], with few works proposing smartphones for respiratory rate monitoring [28,29]. Moreover, an overall kinematic evaluation correlated to the chest-abdominal device position has not been reported.…”

Section: Introductionmentioning

confidence: 99%

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A New Smartphone-Based Method for Remote Health Monitoring: Assessment of Respiratory Kinematics

Vignali,

Gasparotti,

Miglior

et al. 2024

Electronics

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The remote monitoring of clinical parameters plays a fundamental role in different situations, like pandemic health emergencies and post-surgery conditions. In these situations, the patients might be impeded in their movements, and it could be difficult to have specific health monitoring. In recent years, technological advances in smartphones have opened up new possibilities in this landscape. The present work aims to propose a new method for respiratory kinematics monitoring via smartphone sensors. In particular, a specific application was developed to register inertial measurement unit (IMU) sensor data from the smartphone for respiratory kinematics measurement and to guide the user through a specific acquisition session. The session was defined to allow the monitoring of the respiratory movement in five prescribed positions. The application and the sequence were successfully tested on a given population of 77 healthy volunteers. The resulting accelerometers and gyroscope signals were processed to evaluate the significance of differences according to participants’ sex, vector components, and smartphone positioning and, finally, to estimate the respiratory rate. The statistical differences that emerged revealed the significance of information in the different acquisition positions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Smartphone-Based Method for Remote Health Monitoring: Assessment of Respiratory Kinematics

Vignali,

Gasparotti,

Miglior

et al. 2024

Electronics

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“…Preliminary validation data during dynamic activities (walking and running at different speeds) showed good agreement between the presented IMU-based system and a Cosmed K5 metabolic cart, as shown in a work by Angelucci et al . 7 In dynamic conditions, a fine-tuning of the algorithm by Cesareo et al . 17 to adapt the code to the processing of breathing during dynamic activities was added by including the knowledge of the performed activity in the respiratory signal processing.…”

Section: Methodsmentioning

confidence: 99%

An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions

Angelucci

Aliverti

2023

Cardiovasc Eng Tech

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Purpose Breathing parameters change with activity and posture, but currently available solutions can perform measurements only during static conditions. Methods This article presents an innovative wearable sensor system constituted by three inertial measurement units to simultaneously estimate respiratory rate (RR) in static and dynamic conditions and perform human activity recognition (HAR) with the same sensing principle. Two units are aimed at detecting chest wall breathing-related movements (one on the thorax, one on the abdomen); the third is on the lower back. All units compute the quaternions describing the subject’s movement and send data continuously with the ANT transmission protocol to an app. The 20 healthy subjects involved in the research (9 men, 11 women) were between 23 and 54 years old, with mean age 26.8, mean height 172.5 cm and mean weight 66.9 kg. Data from these subjects during different postures or activities were collected and analyzed to extract RR. Results Statistically significant differences between dynamic activities (“walking slow”, “walking fast”, “running” and “cycling”) and static postures were detected (p < 0.05), confirming the obtained measurements are in line with physiology even during dynamic activities. Data from the reference unit only and from all three units were used as inputs to artificial intelligence methods for HAR. When the data from the reference unit were used, the Gated Recurrent Unit was the best performing method (97% accuracy). With three units, a 1D Convolutional Neural Network was the best performing (99% accuracy). Conclusion Overall, the proposed solution shows it is possible to perform simultaneous HAR and RR measurements in static and dynamic conditions with the same sensor system.

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“…Physiological measurements can consider a single parameter, for example, HR, peripheral blood oxygen saturation, respiratory rate [27], and skin temperature, or could be more complex measures combining data from several sensors, such as in the cases of sleep analysis, estimation of energy expenditure, and analysis of physiological parameters with respect to the activity [28]. Even pathophysiological conditions could be accurately detected after appropriate processing of the data, for instance, atrial fibrillation detection [29], high serum glucose level detection, and fall detection [30].…”

Section: Wearables-benefits and Current Limitationsmentioning

confidence: 99%

Fitbit Data to Assess Functional Capacity in Patients Before Elective Surgery: Pilot Prospective Observational Study

Angelucci¹,

Greco²,

Canali³

et al. 2023

J Med Internet Res

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Background Preoperative assessment is crucial to prevent the risk of complications of surgical operations and is usually focused on functional capacity. The increasing availability of wearable devices (smartwatches, trackers, rings, etc) can provide less intrusive assessment methods, reduce costs, and improve accuracy. Objective The aim of this study was to present and evaluate the possibility of using commercial smartwatch data, such as those retrieved from the Fitbit Inspire 2 device, to assess functional capacity before elective surgery and correlate such data with the current gold standard measure, the 6-Minute Walk Test (6MWT) distance. Methods During the hospital visit, patients were evaluated in terms of functional capacity using the 6MWT. Patients were asked to wear the Fitbit Inspire 2 for 7 days (with flexibility of –2 to +2 days) after the hospital visit, before their surgical operation. Resting heart rate and daily steps data were retrieved directly from the smartwatch. Feature engineering techniques allowed the extraction of heart rate over steps (HROS) and a modified version of Non-Exercise Testing Cardiorespiratory Fitness. All measures were correlated with 6MWT. Results In total, 31 patients were enrolled in the study (n=22, 71% men; n=9, 29% women; mean age 76.06, SD 4.75 years). Data were collected between June 2021 and May 2022. The parameter that correlated best with the 6MWT was the Non-Exercise Testing Cardiorespiratory Fitness index (r=0.68; P<.001). The average resting heart rate over the whole acquisition period for each participant had r=−0.39 (P=.03), even if some patients did not wear the device at night. The correlation of the 6MWT distance with the HROS evaluated at 1% quantile was significant, with Pearson coefficient of −0.39 (P=.04). Fitbit step count had a fair correlation of 0.59 with 6MWT (P<.001). Conclusions Our study is a promising starting point for the adoption of wearable technology in the evaluation of functional capacity of patients, which was strongly correlated with the gold standard. The study also identified limitations in the availability of metrics, variability of devices, accuracy and quality of data, and accessibility as crucial areas of focus for future studies.

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A wearable system for respiratory signal filtering based on activity: a preliminary validation

Cited by 9 publications

References 21 publications

A New Smartphone-Based Method for Remote Health Monitoring: Assessment of Respiratory Kinematics

A New Smartphone-Based Method for Remote Health Monitoring: Assessment of Respiratory Kinematics

An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions

Fitbit Data to Assess Functional Capacity in Patients Before Elective Surgery: Pilot Prospective Observational Study

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