Sarah Ostadabbas

A Motion-Tolerant Adaptive Algorithm for Wearable Photoplethysmographic Biosensors

The performance of portable and wearable biosensors is highly influenced by motion artifact. In this paper, a novel real-time adaptive algorithm is proposed for accurate motion-tolerant extraction of heart rate (HR) and pulse oximeter oxygen saturation (SpO2) from wearable photoplethysmographic (PPG) biosensors. The proposed algorithm removes motion artifact due to various sources including tissue effect and venous blood changes during body movements and provides noise-free PPG waveforms for further feature extraction. A two-stage normalized least mean square adaptive noise canceler is designed and validated using a novel synthetic reference signal at each stage. Evaluation of the proposed algorithm is done by Bland-Altman agreement and correlation analyses against reference HR from commercial ECG and SpO2 sensors during standing, walking, and running at different conditions for a single- and multisubject scenarios. Experimental results indicate high agreement and high correlation (more than 0.98 for HR and 0.7 for SpO2 extraction) between measurements by reference sensors and our algorithm.

Bed posture classification for pressure ulcer prevention

Pressure ulcer is an age-old problem imposing a huge cost to our health care system. Detecting and keeping record of the patients posture on bed, help care givers reposition patient more efficiently and reduce the risk of developing pressure ulcer. In this paper, a commercial pressure mapping system is used to create a time-stamped, whole-body pressure map of the patient. An image-based processing algorithm is developed to keep an unobtrusive and informative record of patients bed posture over time. The experimental results show that proposed algorithm can predict patients bed posture with up to 97.7% average accuracy. This algorithm could ultimately be used with current support surface technologies to reduce the risk of ulcer development.

Wireless Medical-Embedded Systems: A Review of Signal-Processing Techniques for Classification

Body-worn sensor systems will help to revolutionize the medical field by providing a source of continuously collected patient data. This data can be used to develop and track plans for improving health (more sleep and exercise), detect disease early, and provide an alert for dangerous events (e.g., falls and heart attacks). The amount of data collected by even a small set of sensors running all day is too much for any person to analyze. Signal processing and classification can be used to automatically extract useful information. This paper presents a general classification framework for wireless medical devices and reviews the available literature for signal processing and classification systems or components used in body-worn sensor systems. Examples focus on electrocardiography classification and signal processing for inertial sensors.

An automatic segmentation technique in body sensor networks based on signal energy

Monitoring human activities using wearable wireless sensor nodes has the potential to enable many useful applications for everyday situations. The long-term lifestyle monitoring can greatly improve healthcare by gathering information about quality of life; aiding the diagnosis and tracking of certain diseases such as Parkinsons. The deployment of an automatic and computationally-efficient algorithm reduces the complexities involved in the detection and recognition of human activities in a distributed system. This paper presents a new algorithm for automatic segmentation of routine human activities. The proposed algorithm can distinguish between discrete periods of activity and rest without specifically knowing the activity. A finite subset of nodes can detect all human activities, but each node by itself can only detect a particular set of activities. For local segmentation we choose the parameters for each node that result in the least segmentation error. We demonstrate the effectiveness of our algorithm on data collected from body sensor networks for a scenario simulating a set of daily activities.

A smart bed platform for monitoring & Ulcer prevention

The focus of this paper is to develop a software-hardware platform that addresses one of the most costly, acute health conditions, pressure ulcers — or bed sores. Caring for pressure ulcers is extremely costly, increases the length of hospital stays and is very labor intensive. The proposed platform collects information from various sensors incorporated into the bed, analyzes the data to create a time-stamped, whole-body pressure distribution map, and commands the beds actuators to periodically adjust its surface profile to redistribute pressure over the entire body. These capabilities are combined to form a cognitive support system, that augments the ability of a care giver, allowing them to provide better care to more patients in less time. For proof of concept, we have implemented algorithms and architectures that cover four key aspects of this platform: 1) data collection, 2) modeling & profiling, 3) machine learning, and 4) acting.

A Resource-Efficient Planning for Pressure Ulcer Prevention

Pressure ulcer is a critical problem for bed-ridden and wheelchair-bound patients, diabetics, and the elderly. Patients need to be regularly repositioned to prevent excessive pressure on a single area of body, which can lead to ulcers. Pressure ulcers are extremely costly to treat and may lead to several other health problems, including death. The current standard for prevention is to reposition at-risk patients every 2 h. Even if it is done properly, a fixed schedule is not sufficient to prevent all ulcers. Moreover, it may result in nurses being overworked by turning some patients too frequently. In this paper, we present an algorithm for finding a nurse-effort optimal repositioning schedule that prevents pressure ulcer formation for a finite planning horizon. Our proposed algorithm uses data from a commercial pressure mat assembled on the beds surface and provides a sequence of next positions and the time of repositioning for each patient.

Pressure ulcer prevention: An efficient turning schedule for bed-bound patients

Pressure ulcer is a critical problem for bed-ridden and wheelchair-bound patients, diabetics, and the elderly. Patients need to be regularly repositioned to prevent excessive pressure on a single area of body, which can lead to ulcers. Pressure ulcers are costly to treat and cause many other health problems, including death. The current standard for prevention is to reposition at-risk patients every two hours. This level of attention is becoming increasingly unrealistic for already overworked nursing staff. In this paper, we present a scheduling algorithm that uses data from a pressure mat on the hospital bed to compute a repositioning schedule that minimizes nursing staff interaction while still preventing pressure ulcer formation. Our experimental results show a 30% increase in the average time between repositioning over the standard schedule. Furthermore, some postures were found to be unsafe if not changed for more than one hour.

In-bed posture classification and limb identification

We propose an algorithm that uses pressure image data to detect a persons sleeping posture and identifies different body limbs. Our algorithm can be used in monitoring bed-bound patients and assessing the risk of pressure ulceration. We used a GMM-based clustering approach for concurrent posture classification and limb identification. Our proposed technique, applied on 9 healthy subjects instructed to sleep in 13 different postures, resulted in 98.4% classification accuracy in distinguishing three main stable sleeping postures. Additionally, 8 limbs in supine and 5 limbs in left/right side postures were identified with the overall accuracy of 91.6%.

Continuous eight-posture classification for bed-bound patients

Pressure ulcer is a prevalent complication for bed-bound patients who are not able to shift their body weights over time. Continuous monitoring of patients postures in the bed can be helpful for caregivers in order to keep track of patients movements and quality of their repositioning during a day. This information allows hospitals to plan an effective repositioning schedule for each patient. In this paper, a high speed and robust posture classification algorithm is proposed that can be employed in any pervasive patients monitoring system. First, a whole-body pressure image is recorded using a commercial pressure mat system. Image enhancement is then applied to the raw pressure images and a binary signature for each different posture is constructed. Finally, using a binary pattern matching technique, a given posture can be classified to one of the known posture classes. Our extensive experiments show that the proposed algorithm is able to predict in-bed postures with more than 97% average accuracy.

A passive quantitative measurement of airway resistance using depth data

The Respiratory Syncytial Virus (RSV) is the most common cause of serious lower respiratory tract infections in infants and young children. RSV often causes increased airway resistance, clinically detected as wheezing by chest auscultation. In this disease, expiratory flows are significantly reduced due to the high resistance in patients airway passages. A quantitative method for measuring resistance can have a great benefit to diagnosis and management of children with RSV infections as well as with other lung diseases. Airway resistance is defined as the lung pressure divided by the airflow. In this paper, we propose a method to quantify resistance through a simple, non-contact measurement of chest volume that can act as a surrogate measure of the lung pressure and volumetric airflow. We used depth data collected by a Microsoft Kinect camera for the measurement of the lung volume over time. In our experimentation, breathing through a number of plastic straws induced different airway resistances. For a standard spirometry test, our volume/flow estimation using Kinect showed strong correlation with the flow data collected by a commercially-available spirometer (five subjects, each performing 20 breathing trials, correlation coefficient = 0.88, with 95% confidence interval). As the number of straws decreased, emulating a higher airway obstruction, our algorithm was sufficient to distinguish between several levels of airway resistance.