Depression is a prevalent mental health disorder characterized by emotional dysregulation, leading to persistent sadness, loss of interest, and anhedonia [1-3]. The rising incidence of depression among older adults has become a significant public health issue [4-6]. Early detection of depression and corresponding intervention are vital for improving mental health outcomes and reducing the overall burden on individuals and health care systems [7-9]. Traditional methods for assessing depression include various approaches that typically require in-person evaluations, specialized training in comprehensive geriatric assessments, and reliance on clinical judgment and questionnaires, which can be challenging and resource-intensive [10-12]. These methods require older adults to visit clinical settings frequently, increasing strain on health care facilities and reducing data collection opportunities. Additionally, many older adults prefer to remain in their homes and be remotely monitored in that environment, highlighting the need for remote care solutions in this demographic [13,14].

Physical activity and mobility are among the important factors in evaluating depression, with strong correlations established between these parameters and depression assessments [1,15]. The advent of the Internet of Things has enabled continuous and remote monitoring of physical activity. Several studies have used statistical methods to analyze the relationship between physical activity, as measured by wearable devices, and depression [16-21]. As the field of artificial intelligence (AI) advances, machine learning models have emerged as promising tools for depression classification using physical activity data [22]. For instance, Adamczyk and Malawski [23] used data from wearable actigraph watches in 3 classification models: logistic regression (LR), support vector machine (SVM), and random forest (RF) comparing automatic and manual feature engineering for depression classification. Bai et al [24] used phone use, sleep data, and step counts from 334 participants, using 2 feature selection methods (L1-based feature selection) and 6 machine learning models (decision tree [DT], k-nearest neighbors, naive Bayes, LR, SVM, and RF) for mood classification. Chikersal et al [25] analyzed data from smartphones and fitness trackers of 138 college students to identify those experiencing depressive symptoms, using nested randomized LR for feature selection and AdaBoost with gradient boosting classifier. Dai et al [26] used heart rate, energy expenditure, sleep, and other activity data from wearable Fitbit devices for depression remission detection in 106 participants within 2 intervention and control groups, using a multitask learning algorithm comprising 2 dense layers with shared parameters. Similarly, Griffiths et al [27] classified depression using activity and sleep data from Fitbit devices of 24 participants through an RF model. Espino-Salinas et al [28] used wrist-worn accelerometers to measure physical activity in 55 participants, applying a 2D-convolutional neural network (CNN) and a deep neural network for depression classification. Jakobsen et al [29] used RF, deep neural network, and CNN algorithms for depression classification with wrist-worn actigraph data from 55 participants. Jung et al [30] used gait accelerometry data and a bidirectional long short-term memory network–based classifier to assess depression in 45 older adults. Other studies have also explored the use of wearable device data combined with various classification methods, such as 1D-CNN [31], deep convolutional neuro-fuzzy [32], Ensemble models [33], and extreme gradient boosting [34].

These recent studies highlight the integration of wearable devices with machine learning algorithms as a promising approach for continuous, remote, home-based monitoring and early detection of depression. However, wearable device-based approaches face challenges due to their intrusive nature [35]. Participants are required to wear sensors or devices, which may lead to issues with compliance, comfort, and data accuracy, especially over extended periods [36], particularly among older adults [37]. These challenges can result in inaccurate data collection [38]. Nonwearable methods, such as remote monitoring through ambient sensors, offer potential solutions to these issues [37,39,40]. These approaches can alleviate concerns related to device adherence and physical discomfort, providing a more seamless integration into daily life. Additionally, our literature review indicates that most studies on depression classification use a relatively large number of participants and primarily use single-stage classifiers. Many studies also focus on detailed aspects of physical activity data (eg, body displacement, acceleration) using intrusive wearable sensors, which, while effective, present challenges related to user comfort and compliance. This reliance on wearable technology underscores the need for exploring alternative, less intrusive methods.

Recently, Wi-Fi–based sensing in smart homes has emerged as an alternative method for detecting and monitoring contextual human activity and movement [41]. Wi-Fi–based technologies are increasingly adopted due to their existing infrastructure in homes and minimal additional setup costs [42]. These technologies use signal metrics such as received signal strength indicator (RSSI) and channel state information (CSI) to analyze Wi-Fi signal characteristics, offering human activity identification compared to invasive wearable sensors, image analysis, or video-based systems [43]. Leveraging Wi-Fi–based activity data with machine learning presents a viable approach for different diseases, specifically depression classification, which is the focus of this study.

Technological and AI-based methods for depression classification predominantly rely on wearable sensors and are often conducted on large participant groups and typically use statistical techniques or single-stage machine learning classifiers. Furthermore, these approaches often overlook the explainability analysis of the models, which causes a lack of understanding of the underlying decision-making processes and the contribution of individual features to the model’s predictions. To address these challenges, this feasibility study introduces a novel 3-stage machine learning model that incorporates feature selection, dimensionality reduction, and classification for depression classification. This model is specifically designed for a limited number of participants using low cost, easily installable Wi-Fi data, which provides continuous insights into human indoor activities. In addition to creating a model that functions effectively with small sample sizes, this study integrates explainable AI techniques to enhance the interpretability of the model’s predictions. This approach ensures that the insights derived from the model are transparent and comprehensible, providing clarity on how specific features contribute to the classification outcomes. Figure 1 illustrates a schematic overview of our proposed framework.

Our research uses Wi-Fi–based motion sensors to extract daily activities, which are then used in our proposed machine learning method for depression classification.

Our study findings confirm the feasibility of using Wi-Fi–based motion sensors for depression classification among older adults. Our proposed HOPE (Home-Based Older Adults’ Depression Prediction) model achieved an accuracy of 87.5%, sensitivity of 90%, and precision of 88.3%. The most influential features identified were sleep-related metrics, such as average sleep duration and sleep interruptions, highlighting the importance of sleep patterns in depression classification. These findings suggest that Wi-Fi–based monitoring offers a nonintrusive and effective alternative to conventional wearable technologies for depression assessment. These conventional methods, while effective, often present challenges in terms of participant compliance, particularly among older adults, due to their burdensome and sometimes uncomfortable nature. In contrast, our Wi-Fi–based approach is nonintrusive and allows for continuous monitoring without requiring participants to wear or interact with any devices. This can significantly enhance participant compliance and the integrity of the data collected over extended periods. Compared to other nonintrusive monitoring technologies, such as camera-based methods, our Wi-Fi–based approach has distinct advantages. Wi-Fi infrastructure is prevalent in most homes and does not pose privacy risks, making it a cost-effective and scalable solution for continuous health monitoring. Furthermore, unlike previous studies that rely on microlevel body displacement and accelerometer data, our study emphasizes macrolevel physical activity features like sleep patterns and overall activity levels shift is crucial as it highlights the potential of using broader, more easily obtainable metrics to assess depression status. Our findings demonstrate that these macrolevel features are not only feasible but also effective measures for depression classification, broadening the scope of nonintrusive monitoring technologies in mental health research. The next steps can be extracting more detailed types of human activity using nonintrusive Wi-Fi data and expanding more on using this type of data acquisition for depression classification.

Additionally, our proposed model demonstrates relatively high performance compared to other classification models presented in existing depression classification studies, even with a limited sample size. Many studies using physical activity data from wearable devices often benefit from larger datasets and frequently use single-stage classifiers or deep neural networks. These models generally show strong performance with abundant data; however, their effectiveness diminishes when applied to smaller datasets, such as the one in this feasibility study. To address the limitations imposed by our smaller sample size, we designed a 3-stage machine learning classification architecture, which combines feature selection, dimensionality reduction, and classification into a multistep process. This approach allows for the extraction of the most relevant features while minimizing noise, thereby improving classification performance. Despite the small sample size, our model consistently outperformed conventional single-stage classifiers, highlighting the strength of both the machine learning architecture and the selected human activity features—particularly sleep patterns and activity levels—used for depression classification. This also underscores the adaptability of our model to different data scales, making it a more versatile option for future research where data availability might be limited. While this model shows promising results, however, caution is needed in interpreting these results. Future work should aim to enhance its robustness and generalizability by expanding the dataset. Collecting Wi-Fi–based physical activity data from a larger and more diverse sample would not only improve the model’s statistical power but also allow for a more comprehensive evaluation of its performance across different population groups, such as varying age ranges and health conditions. This would be particularly valuable in developing a scalable solution for real-world applications. Additionally, the integration of advanced machine learning techniques, such as deep neural networks or hybrid models combining traditional classifiers with deep learning components, could further enhance classification accuracy.

Our study is distinctive not only in its methodological approach but also in its emphasis on model explainability, a crucial aspect often overlooked in prior research on depression classification. Explainability is essential in health care applications, where understanding the factors driving a model’s decision is critical for clinical adoption and trust. By using SHAP and LIME, we were able to dissect the decision-making process of our model and pinpoint the most influential features for classifying depression. Both explainability analyses converge on the identification of sleep interruption features as key predictors in the depression classification of our proposed model. Among these, the “percentage of nights with sleep interruptions,” “average sleep duration,” and “total number of sleep interruptions” emerged as the primary driving factors. These findings align with existing literature that highlights the strong correlation between sleep disturbances and depression. However, our approach goes a step further by quantifying the impact of these features on the classification outcomes, providing a more nuanced understanding of their role. These findings suggest that future tools for depression assessment may benefit from a stronger focus on sleep quality and patterns and further investigations are required in this regard.

The integration of sensors and AI is transforming health care, yet the application of these technologies in depression classification remains underdeveloped and lacks extensive investigation. This study aimed to create an automated machine learning system for the continuous, remote monitoring and assessment of daily physical activity among older adults in a home setting, with the goal of distinguishing between individuals with and without depression.

In summary, although there were some challenges, our results suggest that using Wi-Fi–based data to capture contextual human activities is a promising and efficient method for classifying depression. The model we developed, leveraging data from Wi-Fi motion sensors, showed strong potential in accurately identifying early signs of depression and paving the way for more advanced and accessible mental health monitoring technologies among community dwelling older adults..