# Pattern Recognition in Older Adults' Activities of Daily Living
## 2 Related Work
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Aspect** & **This Work** & **[PERSON] et al. [10]** & **[PERSON] et al. [11]** & **[PERSON] et al. [12]** & **[PERSON] et al. [13]** & **[PERSON] et al. [14]** & **[PERSON] and [PERSON] [15]** & **[PERSON] and [PERSON] [16]** \\ \hline \multirow{4}{*}{Scalability} & Designed for diverse and user groups. & Limited focus on specific anomaly detection in elderly adults activities. & Flood for real activity, school of other use cases. & Tailored for fall detection, not scalable to other use cases. & Limited to detecting group & Focused on indoor systems, systems, scalability not emphasized. & Scalability not a focus, Deligned for dementia monitoring, centralized on primary for home settings. \\ \hline \multirow{4}{*}{User and Energy Needs} & Explicitly addresses both older adults and caregivers & Focused on solely on elderly activities. & High-level IoT applications; does not address user-specific needs. & Caregiver needs not addressed: technical fall detection. & Creceiver needs but not explicitly prioritized. & Caregiver includes not explicitly modified, without caregiver, predictive or specific user requirements. & Leducs on specific user requirements. \\ \hline \multirow{4}{*}{Anomaly Detection} & Efficient patient recognition, minimizing false positives and negatives. & Anomaly targeted but false alerts. & Anomaly detection not a primary & Anomaly detection not applicable to falls; application to falls; application to rollers. & Behavioral characteristics of animals and analyzed, but misclassification in complex detection not a priority. & Minimal focus on anomaly detection not location-based pattern. & Does not explicitly prioritize detection in exploration of error reduction. \\ \hline \multirow{4}{*}{Focus Area} & Comprehensive ADL monitoring system for older adults. & Anomaly detection in olders for elderly individuals. & IoT applications for defense and public safety. & Real-time fall detection using RAN systems. & Smart home data for indoor location & Reaction-based prediction human for behavior with monitoring modeling. & Traffic for behavior with modeling. & Smart home monitoring patients. \\ \hline \multirow{4}{*}{Data Preprocessing} & Robust, including timestamp decomposition, and redundancy measures. & Reiles on statistical and machine techniques for preprocessing. & High-level aggregation, less focus on and real-time preprocessing. & Sensor fusion and real-time temporal filtering filtering for false positives for false positives for transitions. & Preprocessing improves for learning of false positives for learning. & Timestamp grouping and signal processing and sensor for localization. & Timestamp grouping and signal processing and network in neural network in temporal and sensor integration in inputs. & Preprocessed sequential and sensor integration \\ \hline \multirow{4}{*}{Adaptability} & Applicable to independent learning, home, and mobility-impaired users. & Focused on general elderly users. & General elderly applications, healthy, mobile users. & Limited adaptation to broader & Specific to adaptable to healthcare & Primarily indoor-adaptable to focused; limited, adaptable to access access, speed to conditions. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the approach presented in this work with the techniques from the referenced papers, highlighting the key differences and unique contributions. different settings, such as in rural areas or for individuals with special needs. Our method is scalable due to its modular construction and the ability to process large datasets iteratively. The modular manageable steps are retrieving beacon information, assigning compartments, and calculating metrics. This allows for independent scaling of each part.