**5. Discussions and future works**

The results show that the fall detection and activity recognition algorithms are competitive. The fall detection was at 96% minimum accuracy, which is above average for the cited other researchers' works which had an average rate of 89% as shown in **Table 1**. We have used cross verification with other dataset records. This is with the hope that the subject senior citizens' data would have to be recorded and then observed to make the correct adjustments. The activity recognition was the most accurate. It performed at 98.96% for training and 100.0% for the validation session. The general accuracy for the xgboost algorithm was above 96% on each of the three datasets. However, the deep learning method was the most accurate. After a

### *Autonomous Update of a Dataset for Anomaly Detection Services in Elderly Care Smart House DOI: http://dx.doi.org/10.5772/intechopen.103953*

successful classification, we can add the new record and its classification label to the dataset. This thus extends and improves the dataset. We could also include one heuristic to improve accuracy and reduce computational costs. For instance, we should be able to tell that sleeping must have zero chance of occurring in the toilet, hence not consider it a possibility when computing. This allows resources to be concentrated on viable options when performing classification. Eliminating such computation decision cost can fine-turn the system, as it allows only options with high likelihood. The low likelihood options are removed from the allocation of computation resources. This allocation of energy to the option improves efficiency. In the case that one class is larger than another the dataset is unbalanced. In usual cases this system works on unbalanced datasets, hence it was important to have good characteristics in its sensitivity and specificity.

#### **5.1 Edge processing and security**

In this work, the data sensing was performed using non-protruding methods which are a mobile phone and a smartwatch data sensor. However, this method is replaceable in the structure of the system. The data can also be collected by using a camera. A labeled dataset is then used to label extracted images. Mobile collects data and then this data is not processed on the phone but sent to some processing point due to limitations of the processing capability of the phone [26]. The training and testing set labels the images. However, in our project, we use an accelerometer and gyroscope as input sensors. The rest of the system would be the same. A systems camera sensor is an advantage in that it would be easier to label.

#### **5.2 Replacement of training and testing dataset**

When data is collected from the subject because seniors change rapidly for deteriorate help, the trend of that senior would change. Hence the dataset must be properly monitored and adjusted to match the rate of changes in the trend of the senior. Otherwise maintaining the same training data for an extended period would result in an obsolete detection system [20]. Aging can change the gait pattern of an individual hence the importance to update the dataset constantly. Having been limited by the current covid-19 situation we are having difficulty arranging our data collection activities. However, we believe the public datasets, SisFall, MobiAct, and Ucihar have provided good insights into the record we could have managed to collect. We believe these datasets have an unobstructed view of the results we could have obtained. After collecting sensor data and labeling it, the dataset component from Sisfall, MobiAct, and Ucihar databases would be gently removed and replaced with these records. This is a continuous process until the dataset remains pure containing the new record of the senior citizens without the legacy dataset. Erroneous labeled data would continuously be removed to have a robust and current training dataset.

#### **5.3 The medicine routine remainder service**

The medicine remainder is a time-based scheduler. The schedule must be executed correctly and if not, then an alarm is sent. The alarm is triggered based on the failure of sending confirmation of executing the medicine schedule by the senior. The senior must send a confirmation once prompted to do so. However, there is a risk that the subject might be able to falsely confirm they took the medicine when in fact they did

not take it. This system currently cannot help when the senior is specifically not providing the correct state of medicine routine. It is meant to help in cases where participants are willing to take the medicine. An alert will be sent to caregivers allowing them to prompt the senior on the state of their medicine routine.
