# Pattern Recognition in Older Adults' Activities of Daily Living
## 2 Related Work  
The detection of anomalies in behavior is a critical aspect of activities of daily living (ADLs) in older adults. It involves identifying unusual patterns in behavior, which could indicate a potential problem, such as a fall, a medical emergency, or a change in health condition. In recent years, several studies have focused on developing algorithms and methods to detect anomalies in behavior using various sensors and data analysis techniques. In [10], an algorithm to detect falls in older adults using wearable sensors is presented. The algorithm uses a combination of ML techniques and signal processing to detect abnormal movements and postures that are characteristic of falls. The algorithm was evaluated using real-world data collected from older adults, and the results showed high accuracy in detecting falls. In [11], an algorithm is developed to detect anomalies in eating behavior using a smartwatch. The algorithm uses data from the accelerometer and gyroscope sensors in the smartwatch to identify patterns of hand-to-mouth movements, which are indicative of eating behavior. The algorithm was evaluated using data collected from a group of volunteers, and the results showed a high accuracy in detecting eating behavior. In addition to detecting falls and eating behavior, several studies have also focused on detecting anomalies in other ADLs, such as sleeping, walking, and medication adherence. For instance, ref. [12] proposed an algorithm to detect sleep disturbances in older adults using data from a wearable device. The algorithm uses ML techniques and signal processing to analyze data from the device's accelerometer and gyroscope sensors to detect abnormal sleep patterns. Similarly, ref. [13] proposed an algorithm to detect walking anomalies in two different groups of adults: one with cognitive impairment and the other composed of healthy adults. They use a virtual classifier algorithm that uses data from GPS sensors to identify walking speed changes, which indicate walking abnormalities. The results show a high level of accuracy in detecting walking anomalies. Using both CNN and LSTM architectures, ref. [14; 15] can predict a person's behavior while representing actions with neural embedding. Finally, ref. [16] proposed an algorithm to detect medication adherence in older adults using data from a smartwatch. The algorithm uses data from the smartwatch's accelerometer and gyroscope sensors to detect hand-to-mouth movements that indicate taking medication. The algorithm was evaluated using data collected from a group of older adults, and the results showed a high level of accuracy in detecting medication adherence. In conclusion, detecting anomalies in behavior is an essential aspect of monitoring the ADLs of older adults. It involves using sensors and data analysis techniques to identify unusual patterns in behavior that could indicate a potential problem. Several studies have focused on developing algorithms and methods to detect anomalies in behavior using various sensors and data analysis techniques, with promising results. These studies can potentially improve the quality of life of older adults and provide valuable insights into their health and well-being. Table 1 compares the approach taken in this work and those from the literature. The contribution of the present work lies in its ability to provide a scalable solution for monitoring the ADLs of older adults. While there have been several works in the past that have attempted to address the same issue, what sets our solution apart is its ability to cater to diverse users. One of the critical challenges previous works have faced is the need for more scalability of their solutions. This has made it challenging to apply their solutions in