**6. Conclusion**

We commend this system can evaluate and monitor the situation and status of the senior citizen in their apartment. This system has been designed using three main algorithms. First algorithm tests if a fall has occurred or not. Eight different common algorithms are evaluated to see which one is most effective. If a fall is detected, then the caregiver is alerted. The second algorithm is to identify what activity is the senior citizen doing. This algorithm has the purpose of detecting the location where activity is occurring. If an activity such as sleeping or laying is taking place in the wrong location an alert is evoked. The last algorithm is detecting whether the medicine routine prescribed is been executed or not. The system utilizes a dispenser that can record if a pill was extracted from the pill dispenser or not. This is checked at a specified period as scheduled. When there is no change in the medical container after the expected pill dispensation time elapsed, then an alert warning message is sent to caregivers. The medical remainder also enables caregivers to prompt subject seniors to implement the medicine routine, when medicine taking has been skipped.

These three algorithms are the primary functions of this smart house design. The system utilizes the executed algorithms to effect detection. The system must use few resources and utilize the improved performance algorithm. The design would be updated with specific data when training for a specific client. The sample, data is also to be replaced with the change in the pattern of the senior citizen trends. The final efficiency in the algorithms improves from 96–98% in the training session and the validation is at 85–100% for the testing session. Since the customized records are generated from the citizen, the training and validation are guaranteed to improve at every iteration. With a sensitivity minimum of 78.66% and a specificity minimum of 97.86%, the model is performing well as the dataset used is not balanced.

This prototype would allow using the most effective classifier and dynamically determine the most effective classifier. Dynamic evaluation of algorithm efficiency should be integrated, as the accuracy would not always be the best since training data is updated periodically. Using the location variable may also reduce the computing resources needed if integrated into the classifier algorithm. Without a logic heuristic, the computation process would require more resources.
