# In-Home Evaluation of the Neo Care Artificial Intelligence Sound-Based Fall Detection System
## 1 Introduction  
Falls among the elderly constitute a burgeoning public health crisis with far-reaching implications for both individuals and healthcare systems [1]. These incidents often result in a spectrum of adverse outcomes, ranging from minor abrasions and bone fractures to severe complications, such as loss of consciousness or even fatality [2, 3]. Timely medical intervention is imperative, not only to alleviate the immediate physical and psychological trauma, but also to curtail the escalating burden on healthcare resources [4, 5]. Monitoring falls, particularly within the domestic setting, presents unique challenges [6]. Conventional wearable technologies, like alert pendants, are limited in their efficacy. Their utility is predicated on the user's ability to activate them, which is not possible if the individual is unconscious or incapacitated during the fall [6]. Additionally, the stigma associated with these devices or the fear of being a burden may deter their usage among the elderly [7]. Recent advancements in artificial intelligence (AI) and machine learning offer promising avenues for enhancing fall detection systems. Smartwatches, for instance, come equipped with sophisticated algorithms capable of autonomously alerting emergency services. However, these devices inherit the fundamental limitation of all wearables: theymust be worn to function. Moreover, their algorithms are not foolproof and are susceptible to false positives and missed detections [8]. A prior systematic review [9] that sought to examine the existing literature on the use of AI techniques for analyzing data pertaining to postural control and fall risk assessment identified and included a total of 22 relevant studies. The findings showed that various AI techniques, including machine learning models (logistic regression, decision trees, random forests, boosted trees, and support vector machines) and deep learning models (convolutional neural networks [CNNs], long short-term memory [LSTM] networks, and associative skill memories) were used to develop predictive models for classifying the risk of falls [9]. The studies demonstrated the feasibility and promising accuracy of these AI approaches, with most achieving over 70% accuracy in fall risk prediction [9]. Techniques like XGBoost, random forests with feature engineering, CNNs, and LSTMs were found to be particularly effective [9]. Several studies compared different AI methods or combined multiple parameters, such as gait-related features and seated position data, to improve classification accuracy. The highest reported accuracy was 92.7% using CNNs to differentiate between normal gait and fall events [9]. While in a previous study, [PERSON] et al. [10] evaluated an \"Internet of Things\" (IoT) elderly fall detection system leveraging wearable accelerometers, big data analytics from the MobiAct dataset, cloud computing with Apache Flink, and LSTM machine learning models on an IoT gateway, achieving 95.87% real-time fall detection accuracy to improve elderly care and independence. Overall, the findings highlighted the potential for applying various AI techniques to the issues of falls. To date, the most common approaches are either through accelerometry-based wearables, or video-based systems. Overall, previous research has predominantly involved wearable sensors or accelerometers [11; 12; 13; 14; 15], studies utilizing tests or assessments in combination with camera-based systems [16; 17], or studies relying solely on data from medical records [18]. The use of smart speakers equipped with AI models for audio-based fall detection could offer a more convenient and unobtrusive approach to monitoring fall risk, particularly in home settings. Environmental monitoring systems, leveraging the IoT, offer an alternative approach. These systems monitor routine activities through smart appliances and can trigger alerts based on significant deviations or anomalies [19].