Md Zobaer Islam

Md Zobaer Islam, Brenden Martin, Carly Gotcher et al.

Human respiratory rate and its pattern convey essential information about the physical and psychological states of the subject. Abnormal breathing can indicate fatal health issues leading to further diagnosis and treatment. Wireless light-wave sensing (LWS) using incoherent infrared light shows promise in safe, discreet, efficient, and non-invasive human breathing monitoring without raising privacy concerns. The respiration monitoring system needs to be trained on different types of breathing patterns to identify breathing anomalies.The system must also validate the collected data as a breathing waveform, discarding any faulty data caused by external interruption, user movement, or system malfunction. To address these needs, this study simulated normal and different types of abnormal respiration using a robot that mimics human breathing patterns. Then, time-series respiration data were collected using infrared light-wave sensing technology. Three machine learning algorithms, decision tree, random forest and XGBoost, were applied to detect breathing anomalies and faulty data. Model performances were evaluated through cross-validation, assessing classification accuracy, precision and recall scores. The random forest model achieved the highest classification accuracy of 96.75% with data collected at a 0.5m distance. In general, ensemble models like random forest and XGBoost performed better than a single model in classifying the data collected at multiple distances from the light-wave sensing setup.

Russ Messenger, Md Zobaer Islam, Matthew Whitlock et al.

Highway work zones are susceptible to undue accumulation of motorized vehicles which calls for dynamic work zone warning signs to prevent accidents. The work zone signs are placed according to the location of the end-of-queue of vehicles which usually changes rapidly. The detection of moving objects in video captured by Unmanned Aerial Vehicles (UAV) has been extensively researched so far, and is used in a wide array of applications including traffic monitoring. Unlike the fixed traffic cameras, UAVs can be used to monitor the traffic at work zones in real-time and also in a more cost-effective way. This study presents a method as a proof of concept for detecting End-of-Queue (EOQ) of traffic by processing the real-time video footage of a highway work zone captured by UAV. EOQ is detected in the video by image processing which includes background subtraction and blob detection methods. This dynamic localization of EOQ of vehicles will enable faster and more accurate relocation of work zone warning signs for drivers and thus will reduce work zone fatalities. The method can be applied to detect EOQ of vehicles and notify drivers in any other roads or intersections too where vehicles are rapidly accumulating due to special events, traffic jams, construction, or accidents.

Md Zobaer Islam, Li Yu, Hisham Abuella et al.

In this study, we present a wireless (non-contact) gesture recognition method using only incoherent light wave signals reflected from a human subject. In comparison to existing radar, light shadow, sound and camera-based sensing systems, this technology uses a low-cost ubiquitous light source (e.g., infrared LED) to send light towards the subject's hand performing gestures and the reflected light is collected by a light sensor (e.g., photodetector). This light wave sensing system recognizes different gestures from the variations of the received light intensity within a 20-35cm range. The hand gesture recognition results demonstrate up to 96% accuracy on average. The developed system can be utilized in numerous Human-computer Interaction (HCI) applications as a low-cost and non-contact gesture recognition technology.

Md Zobaer Islam, Brenden Martin, Carly Gotcher et al.

In this study, we present a non-contact respiratory anomaly detection method using incoherent light-wave signals reflected from the chest of a mechanical robot that can breathe like human beings. In comparison to existing radar and camera-based sensing systems for vitals monitoring, this technology uses only a low-cost ubiquitous infrared light source and sensor. This light-wave sensing system recognizes different breathing anomalies from the variations of light intensity reflected from the chest of the robot within a 0.5m-1.5m range with an average classification accuracy of up to 96.6% using machine learning.

Md Zobaer Islam, Sabit Ekin, John F. O'Hara et al.

In this study, we present a deep learning-based approach for time-series respiration data classification. The dataset contains regular breathing patterns as well as various forms of abnormal breathing, obtained through non-contact incoherent light-wave sensing (LWS) technology. Given the one-dimensional (1D) nature of the data, we employed a 1D convolutional neural network (1D-CNN) for classification purposes. Genetic algorithm was employed to optimize the 1D-CNN architecture to maximize classification accuracy. Addressing the computational complexity associated with training the 1D-CNN across multiple generations, we implemented transfer learning from a pre-trained model. This approach significantly reduced the computational time required for training, thereby enhancing the efficiency of the optimization process. This study contributes valuable insights into the potential applications of deep learning methodologies for enhancing respiratory anomaly detection through precise and efficient respiration classification.