Development of a robust and cost-effective 3D respiratory motion monitoring system using the kinect device: Accuracy comparison with the conventional stereovision navigation system

A Smart Ski Pole for Skiing Pattern Recognition and Quantification Application

In cross-country skiing, ski poles play a crucial role in technique, propulsion, and overall performance. The kinematic parameters of ski poles can provide valuable information about the skier's technique, which is of great significance for coaches and athletes seeking to improve their skiing performance. In this work, a new smart ski pole is proposed, which combines the uniaxial load cell and the inertial measurement unit (IMU), aiming to provide comprehensive data measurement functions more easily and to play an auxiliary role in training. The ski pole can collect data directly related to skiing technical actions, such as the skier's pole force, pole angle, inertia data, etc., and the system's design, based on wireless transmission, makes the system more convenient to provide comprehensive data acquisition functions, in order to achieve a more simple and efficient use experience. In this experiment, the characteristic data obtained from the ski poles during the Double Poling of three skiers were extracted and the sample t-test was conducted. The results showed that the three skiers had significant differences in pole force, pole angle, and pole time. Spearman correlation analysis was used to analyze the sports data of the people with good performance, and the results showed that the pole force and speed (r = 0.71) and pole support angle (r = 0.76) were significantly correlated. In addition, this study adopted the commonly used inertial sensor data for action recognition, combined with the load cell data as the input of the ski technical action recognition algorithm, and the recognition accuracy of five kinds of cross-country skiing technical actions (Diagonal Stride (DS), Double Poling (DP), Kick Double Poling (KDP), Two-stroke Glide (G2) and Five-stroke Glide (G5)) reached 99.5%, and the accuracy was significantly improved compared with similar recognition systems. Therefore, the equipment is expected to be a valuable training tool for coaches and athletes, helping them to better understand and improve their ski maneuver technique.

Spinal alignment measurement with Kinect sensor is valid for thoracic kyphosis but not for lumbar lordosis

BACKGROUND

Spinal alignment evaluation is commonly performed in the clinical setting during rehabilitation. However, there is no simple method for its quantitative measurement. Recently, the depth cameras in Kinect sensors have been employed in various commercial and research projects in the healthcare field. We hypothesized that the time-of-flight technology of the Kinect sensor could be applied to quantitatively evaluate spinal alignment. The purpose of this study was to develop a simple and noninvasive evaluation for spinal alignment using the Kinect sensor and to investigate its validity.

METHODS

Twenty-four healthy men participated in the study. Measurement outcomes were the thoracic kyphosis and lumbar lordosis angles in the standing position, using a Spinal Mouse, the validity of which has been previously reported, and the Kinect sensor. In the measurement by the Kinect sensor, a program was created to obtain the three-dimensional coordinates of each point within an area marked on the monitor, and the sums of the angles at each vertebral level were calculated for the thoracic and lumbar areas. Pearson's correlation coefficient was used to analyze the relationship between the Kinect sensor and Spinal Mouse measurements of thoracic kyphosis and lumbar lordosis angles.

RESULTS

There was a significant positive and moderate correlation between the thoracic kyphosis measurements taken by each device. Contrarily, there was no significant correlation in the lordosis angle between measurements using the Kinect sensor and Spinal Mouse.

CONCLUSIONS

Our results demonstrated the validity of measuring the thoracic kyphosis angle using the Kinect sensor. This indicates that the depth camera in the Kinect sensor is able to perform accurate thoracic alignment measurements quickly and noninvasively.

Clinical validation of a contactless respiration rate monitor

Respiratory rate (RR) is an often underestimated and underreported vital sign with tremendous clinical value. As a predictor of cardiopulmonary arrest, chronic obstructive pulmonary disease (COPD) exacerbation or indicator of health state for example in COVID-19 patients, respiratory rate could be especially valuable in remote long-term patient monitoring, which is challenging to implement. Contactless devices for home use aim to overcome these challenges. In this study, the contactless Sleepiz One+ respiration monitor for home use during sleep was validated against the thoracic effort belt. The agreement of instantaneous breathing rate and breathing rate statistics between the Sleepiz One+ device and the thoracic effort belt was initially evaluated during a 20-min sleep window under controlled conditions (no body movement) on a cohort of 19 participants and secondly in a more natural setting (uncontrolled for body movement) during a whole night on a cohort of 139 participants. Excellent agreement was shown for instantaneous breathing rate to be within 3 breaths per minute (Brpm) compared to thoracic effort band with an accuracy of 100% and mean absolute error (MAE) of 0.39 Brpm for the setting controlled for movement, and an accuracy of 99.5% with a MAE of 0.48 Brpm for the whole night measurement, respectively. Excellent agreement was also achieved for the respiratory rate statistics over the whole night with absolute errors of 0.43, 0.39 and 0.67 Brpm for the 10th, 50th and 90th percentiles, respectively. Based on these results we conclude that the Sleepiz One+ can estimate instantaneous respiratory rate and its summary statistics at high accuracy in a clinical setting. Further studies are required to evaluate the performance in the home environment, however, it is expected that the performance is at similar level, as the measurement conditions for the Sleepiz One+ device are better at home than in a clinical setting.

The use of time-of-flight camera to assess respiratory rates and thoracoabdominal depths in patients with chronic respiratory disease

INTRODUCTION

Over the last 5 years, the analysis of respiratory patterns presents a growing usage in clinical and research purposes, but there is still currently a lack of easy-to-use and affordable devices to perform such kind of evaluation.

OBJECTIVES

The aim of this study is to validate a new specifically developed method, based on Kinect sensor, to assess respiratory patterns against spirometry under various conditions.

METHODS

One hundred and one participants took parts in one of the three validations studies. Twenty-five chronic respiratory disease patients (14 with chronic obstructive pulmonary disease (COPD) [65 ± 10 years old, FEV₁  = 37 (15% predicted value), VC = 62 (20% predicted value)], and 11 with lung fibrosis (LF) [64 ± 14 years old, FEV₁  = 55 (19% predicted value), VC = 62 (20% predicted value)]) and 76 healthy controls (HC) were recruited. The correlations between the signal of the Kinect (depth and respiratory rate) and the spirometer (tidal volume and respiratory rate) were computed in part 1. We then included 66 HC to test the ability of the system to detect modifications of respiratory patterns induced by various conditions known to modify respiratory pattern (cognitive load, inspiratory load and combination) in parts 2 and 3.

RESULTS

There is a strong correlation between the depth recorded by the Kinect and the tidal volume recorded by the spirometer: r = 0.973 for COPD patients, r = 0.989 for LF patients and r = 0.984 for HC. The Kinect is able to detect changes in breathing patterns induced by different respiratory disturbance conditions, gender and oral task.

CONCLUSIONS

Measurements performed with the Kinect sensors are highly correlated with the spirometer in HC and patients with COPD and LF. Kinect is also able to assess respiratory patterns under various loads and disturbances. This method is affordable, easy to use, fully automated and could be used in the current clinical context. Respiratory patterns are important to assess in daily clinics. However, there is currently no affordable and easy-to-use tool to evaluate these parameters in clinics. We validated a new system to assess respiratory patterns using the Kinect sensor in patients with chronic respiratory diseases.

Contactless Monitoring of Breathing Pattern and Thoracoabdominal Asynchronies in Preterm Infants Using Depth Cameras: A Feasibility Study

Objective: Monitoring infants’ breathing activity is crucial in research and clinical applications but remains a challenge. This study aims to develop a contactless method to monitor breathing patterns and thoracoabdominal asynchronies in infants inside the incubator, using depth cameras. Methods: We proposed an algorithm to extract the 3D displacements of the ribcage and abdomen from the analysis of depth images. We evaluated the accuracy of the system in-vitro vs. a reference motion capture analyzer. We also conducted a feasibility study on 12 patients receiving non-invasive respiratory support to estimate the mean and the variability of the chest wall displacements in preterm infants and evaluate the suitability of the proposed system in the clinical setting. Results: In-vitro, the mean (95% CI) error in the measurement of amplitude, frequency and phase shift between compartmental displacements was −0.14 (−0.57, 0.28) mm, 0.02 (−0.99, 1.03) bpm, and −0.40 (−1.76, 0.95)°, respectively. In-vivo, the mean (95% CI) amplitude of the ribcage and abdomen displacements were 0.99 (0.34, 2.67) mm and 1.20 (0.40, 2.15) mm, respectively. Conclusions: The developed system proved accurate in-vitro and was suitable for the clinical environment. Clinical Impact: The proposed method has value for evaluating infants’ breathing patterns in research applications and, after further development, may represent a simple monitoring tool for infants’ respiratory activity inside the incubator.

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20-30% of COVID patients require hospitalization, while almost 5-12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne-Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic.

RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices

Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations.

Design and Evaluation of a MEMS Magnetic Field Sensor-Based Respiratory Monitoring and Training System for Radiotherapy

The patient’s respiratory pattern and reproducibility are important factors affecting the accuracy of radiotherapy for lung cancer or liver cancer cases. Therefore, respiration training is required to induce respiration regularity before radiotherapy. However, the need for specialized personnel, space, and time-consuming training represent limitations. To solve these problems, we have developed a respiratory monitoring and training system based on a micro-electro-mechanical-system (MEMS) magnetic sensor. This system consists of a small attaching magnet, a sensor, and a breathing pattern output device. In this study, we evaluated the performance of the signal measurement in the developed system based on the various respiratory cycles, the amplitudes, and the position angles of the magnet and the sensor. The system can provide a more accurate breathing signal graph with lower measurement error and higher spatial resolution than conventional sensor methods by using additional magnet. In addition, it is possible the patient to monitor and train breathing himself by making it easy to carry and use without restriction of time and space.