Rasoul Yousefi

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.

Adaptive cancellation of motion artifact in wearable biosensors

The performance of wearable biosensors is highly influenced by motion artifact. In this paper, a model is proposed for analysis of motion artifact in wearable photoplethysmography (PPG) sensors. Using this model, we proposed a robust real-time technique to estimate fundamental frequency and generate a noise reference signal. A Least Mean Square (LMS) adaptive noise canceler is then designed and validated using our synthetic noise generator. The analysis and results on proposed technique for noise cancellation shows promising performance.

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.

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 Posture Scheduling Algorithm Using Constrained Shortest Path to Prevent Pressure Ulcers

Pressure ulcer is a severe threat for immobilized and peripheral neuropathic patients such as bed-ridden, elderly, and diabetics. Once developed, the complication of pressure ulcer causes pain, suffering, and longer hospitalization for the patients. Additionally, pressure ulcer management imposes a serious burden on the health care providers. The optimal strategy to deal with pressure ulcers is prevention. The current standard for prevention is to reposition at-risk patients every two hours. But, each patient has different needs based on overall vulnerability and damaged skin areas. A fixed schedule may either result in some patients getting ulcers, or nurses being overworked by turning some patients too frequently. In this paper, we present an efficient algorithm to find a repositioning schedule for bed-bound patients based on their risk of ulcer development. 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. Our patient-specific turning schedule minimizes the overall cost of nursing staff involvement in repositioning the patients while simultaneously decreases the chance of pressure ulcer formation.

Separating Arterial and Venous-Related Components of Photoplethysmographic Signals for Accurate Extraction of Oxygen Saturation and Respiratory Rate

We propose an algorithm for separating arterial and venous-related signals using second-order statistics of red and infrared signals in a blind source separation technique. The separated arterial signal is used to compute accurate arterial oxygen saturation. We have also introduced an algorithm for extracting the respiratory pattern from the extracted venous-related signal. In addition to real-time monitoring, respiratory rate is also extracted. Our experimental results from multiple subjects show that the proposed separation technique is extremely useful for extracting accurate arterial oxygen saturation and respiratory rate. Specifically, the breathing rate is extracted with average root mean square deviation of 1.89 and average mean difference of

Detecting high-risk regions for pressure ulcer risk assessment

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