A straightforward and inexpensive optical nonsurgical technology, PPG(Photoplethysmography) is often used to monitor volumetric changes in blood in the peripheral circulation of humans. It utilizes a luminous source and a light detector at the surface of the skin. In the past few years, a thriving interest has been seen in many researchers around the world in extracting more relevant information from the PPG signal. The second derivative wave of the Photoplethysmography signal offers vital information on the subject's health. Therefore, examination of this waveform may assist doctors and researchers in diagnosing a variety of cardiovascular conditions, such as atherosclerosis and arterial stiffness, which affect the cardiovascular system. Investigating the Photoplethysmography signal's second derivative wave also helps in the early identification and diagnosis of cardiovascular diseases, some of which may not manifest themselves until later stages. Uninterrupted and instantaneous monitoring is an essential comfort that has been made possible because of current technological advancements in sensor technology and wireless networks. This monitoring method allows for early diagnosis and analysis of diseases of this kind. The purpose of this article is first to take a cursory look at some of the recent advancements in wearable PPG-based monitoring technologies as well as some of the issues that come along with them, and pointing some of the possible uses of this technology in clinical settings.

Human gait parameters reveal a lot about physical and psychological well-being. In addition, gait impairments significantly affect daily life activities and hamper the locomotive freedom of people with neurological or musculoskeletal disorders. However, there is still a need for a portable, user-friendly, costeffective gait characterization device. Therefore, in this study, a feature engineering-based portable gait characterization module is proposed, and a shank-mounted inertial measurement unit (IMU) is utilized for gait phases and event detection. The efficiency of the developed module is estimated on ten healthy subjects for plain terrain walking. A force sensing resistor (FSR) sensorized instrumented insole has been utilised as a reference system to validate the results estimated using the developed module. The performance is estimated with three different classifiers, support vector machine (SVM), K-nearest neighbor (KNN), and linear discriminant analysis (LDA). For gait event identifications, the average classification accuracies depicted by SVM, LDA, and KNN classifiers are 95.69±5.23%, 96.64±5.02%, and 93.63±4.84% (p − value < 0.05), respectively. Furthermore, the confusion matrix demonstrated the insight illustration of predicted and misclassified events for individual classifiers. In summary, the gait events and gait temporal parameters are reliably estimated using a single IMU with SVM or LDA classifier (p − value > 0.05). Additionally, the efficacy of the proposed model for sensor location and subject variability has been evaluated. The performance of LDA and SVM classifier for gait phase prediction has been found invariant (p − value > 0.05) towards sensor location and subject variability.

Obstructive sleep apnea (OSA) is a sleep condition characterized by the partial or full interruption of breathing while a person is asleep. Several techniques have been suggested for the automated recognition of OSA events. However, there has been little research on the prediction of these events using prior data, which is valuable for the advancement of medical devices that observe respiration while sleeping. In this research, we have proposed four techniques for forecasting OSA events by using a fusion of time-frequency representation (TFR) of electrocardiogram (ECG) signals and deep neural networks (Gabor-CNN, Wavelet-CNN, Wigner-CNN, and STFT-CNN). Overall, these techniques are called Predict-OSA. These techniques use raw single-lead ECG data collected at a sampling rate of 100 Hz without the inclusion of any manually extracted features. By analyzing the preceding 120 seconds of data, we can predict the occurrences of OSA (apnea) and regular breathing episodes in a lead time of 60 seconds. The findings obtained from analyzing a dataset consisting of more than 34270 60-second segments from 70 ECG recordings indicate that all four models had a high level of accuracy, surpassing 83%. The Wigner-CNN model demonstrated superior performance, achieving accuracy, specificity, and sensitivity of 89.75%, 91.89%, and 86.25%, respectively. The findings indicate that OSA occurrences may be reliably anticipated by analyzing single-lead ECG signals, which creates possibilities for designing devices that can prevent the incidence of OSA events from occurring during sleep. Our algorithms may be implemented in devices with limited storage space due to their ability to handle low sampling rates. This makes them well-suited for use in at-home environments.

Background: Freezing of gait (FoG) is a common and debilitating symptom in individuals with advanced Parkinson’s disease (PD), significantly increasing the risk of falls. Wearable devices have facilitated the detection of FoG and falls, but early prediction remains underexplored. This study investigates the use of multimodal sensor fusion and deep learning for the early prediction of FoG events in PD patients. Research Question: Can a multimodal sensor fusion deep learning model accurately predict FoG events well before time in Parkinson’s disease patients, and how robust is the model to noise and inter-subject variability? Methods: The proposed study utilized Inertial Measurement Unit (IMU), Electromyography (EMG), and Electroencephalography (EEG) signals from PD patients to develop and evaluate deep learning models. The CNN+LSTM architecture was employed and compared with other classifiers. Stratified ten-fold cross-validation was used to assess model accuracy. The robustness of IMU+EMG and IMU+EMG+EEG configurations to noise was tested, and inter-subject performance evaluation was conducted. Pre-FOG detection capabilities were also analyzed to emphasize the importance of temporal dynamics in the multimodal approach. Results: The CNN+LSTM model achieved a high accuracy of 94.45% in predicting FoG events. The IMU+EMG and IMU+EMG+EEG configurations demonstrated robust performance across inter-subject evaluations. The models showed resilience to noise, with the CNN+LSTM and IMU+EMG+EEG configurations maintaining high accuracy. Pre-FOG detection achieved 94.20% accuracy, highlighting the model’s effectiveness in capturing temporal dynamics. Significance: The CNN+LSTM model, particularly in the IMU+EMG+EEG configuration, proves to be a robust and accurate predictor of FoG events in PD patients. The study’s findings underscore the potential clinical impact of multimodal sensor fusion and deep learning in reducing false positives and negatives and enhancing precision, sensitivity, and specificity. These insights are crucial for deploying reliable FoG prediction systems in real-world settings and advancing PD management. Future research should explore additional sensor modalities, transferability to different PD cohorts, longitudinal data, and real-time deployment in clinical environments.