Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed.

In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signal epochs. Such parameters were used as features for the classifiers in our study. We analyzed 69 seizure and 55 non-seizure recordings and an additional 10 continuous recordings from Thomas Jefferson University Hospital, alongside a larger dataset from the CHB-MIT database. By dividing EEGs into epochs (1 s, 2 s, 5 s, and 10 s) and employing fifth-order state-space dynamic systems for feature extraction, we tested various classifiers, with the decision tree and 1 s epochs achieving the highest performance: 96.0% accuracy, 92.7% sensitivity, and 97.6% specificity based on the Jefferson dataset. Moreover, as the epoch length increased, the accuracy dropped to 94.9%, with a decrease in sensitivity to 91.5% and specificity to 96.7%. Accuracy for the CHB-MIT dataset was 94.1%, with 87.6% sensitivity and 97.5% specificity. The subject-specific cases showed improved results, with an average of 98.3% accuracy, 97.4% sensitivity, and 98.4% specificity. The average false detection rate per hour was 0.5 ± 0.28 in the 10 continuous EEG recordings. This study suggests that using a system identification technique, specifically, state-space modeling, combined with machine learning classifiers, such as decision trees, is an effective and efficient approach to automated seizure detection.

Balance perturbations are accompanied by global cortical activation that increases in magnitude when postural perturbations are unexpected, potentially due to the addition of a startle response. A specific site for best recording the response to unexpected destabilization has not been identified. We hypothesize that a single sensor located near to subcortical brainstem mechanisms could serve as a marker for the response to unpredictable postural events. Twenty healthy young (20.8 ± 2.9 yrs) and 20 healthy elder (71.7 ± 4.2 yrs) adults stood upright on a dynamic platform with eyes open. Platform translations (20 cm at 100 cm/s) were delivered in the posterior (29 trials) and anterior (5 catch trials) directions. Active EEG electrodes were located at Fz and Cz and bilaterally on the mastoids. Following platform acceleration onset, 300 ms of EEG activity from each trial was detrended, baseline-corrected, and normalized to the first trial. Average Root-Mean-Square (RMS) values across “unpredictable” and “predictable” events were computed for each channel. EEG RMS responses were significantly greater with unpredictable than predictable disturbances: Cz ( $\text{p} < 0.001$ ), Fz ( $\text{p} < 0.003$ ), and mastoid ( $\text{p} < 0.0001$ ). EEG RMS responses were also significantly greater in elderly than young adults at Cz ( $\text{p} < 0.02$ ) and mastoid ( $\text{p} < 0.04$ ). A significant effect of sex in the responses at the mastoid sensors ( $\text{p} < 0.04$ ) revealed that elderly male adults were principally responsible for the age effect. These results confirm that the cortical activity resulting from an unexpected postural disturbance could be portrayed by a single sensor located over the mastoid bone in both young and elderly adults.

Millions of people around the globe are impacted by falls annually, making it a significant public health concern. Falls are particularly challenging to detect in real time, as they often occur suddenly and with little warning, highlighting the need for innovative detection methods. This study aimed to assist in the advancement of an accurate and efficient fall detection system using electroencephalogram (EEG) data to recognize the reaction to a postural disturbance. We employed a state-space-based system identification approach to extract features from EEG signals indicative of reactions to postural perturbations and compared its performance with those of traditional autoregressive (AR) and Shannon entropy (SE) methods. Using EEG epochs starting from 80 ms after the onset of the event yielded improved performance compared with epochs that started from the onset. The classifier trained on the EEG data achieved promising results, with a sensitivity of up to 90.9%, a specificity of up to 97.3%, and an accuracy of up to 95.2%. Additionally, a real-time algorithm was developed to integrate the EEG and accelerometer data, which enabled accurate fall detection in under 400 ms and achieved an over 99% accuracy in detecting unexpected falls. This research highlights the potential of using EEG data in conjunction with other sensors for developing more accurate and efficient fall detection systems, which can improve the safety and quality of life for elderly adults and other vulnerable individuals.

Electroencephalography (EEG) is a non-invasive technique to record brain electrical activity. It involves placing electrodes on the scalp, which detects the electrical signals generated by neurons in the brain. EEG signals are complex and highly variable. And analyzing them requires sophisticated mathematical and computational techniques. Dynamic systems and machine learning approaches have been widely used in EEG signal processing due to their ability to capture complex and nonlinear relationships in the data. State-space-based dynamic systems theory involves modeling the EEG signal as a system of nonlinear differential equations, i.e., a state space, which can capture the underlying dynamics of the signal. The features extracted from dynamic systems models can then be used as inputs to machine learning algorithms such as decision trees, support vector machines, and deep neural networks. Machine learning algorithms can learn complex patterns and relationships in the data, which can be difficult to capture using traditional signal processing techniques. One of the most critical applications of EEG is in detecting epileptic seizures. Seizures are caused by abnormal electrical activity in the brain and can be challenging to detect using traditional clinical methods. EEG signals can provide a high temporal resolution and a direct measure of brain activity, making them an excellent tool for seizure detection. However, experienced physicians must spend considerable time and effort examining the EEG to make a diagnosis. Therefore, an automatic system that detects and annotates seizures by analyzing EEG would be beneficial. One advantage of automated EEG-based seizure detection is that it can provide real-time monitoring of brain activity, allowing for early detection and intervention. Furthermore, automated EEG-based seizure detection can customize patient treatment plans, optimizing their medication and reducing the risk of adverse effects. Another vital application of EEG is in fall detection, a widespread problem among older adults and people with mobility impairments. Falls can result in serious injuries, hospitalization, and reduced mobility, affecting the overall quality of life. Fall detection systems and devices, including inertial and ambient sensor-based systems, have been proposed to stress this issue. Although these methods can accurately detect abnormal activities, the challenge of distinguishing actual falls from activities of daily living (ADLs) persists. Wearable EEG devices may solve the challenge of accurately detecting real falls. Balance perturbations are accompanied by global cortical activation after perturbation onset. Evidence indicates that the disturbance's predictability modifies such EEG activity. Such findings highlight the potential of using EEG data with other sensors to develop more accurate and efficient fall detection systems, improving the safety and quality of life for older adults and other vulnerable populations. To sum up, this combined approach provides a powerful and flexible way to analyze and interpret EEG signals, allowing for real-time processing and improved performance compared to traditional signal processing techniques.