## 4 Discussion and Conclusions  
The data visualization captured various aspects of raw sensor data, each plot enabling a different interpretation for each activity. The differences in each visual representation enable us to distinguish between different activities, and this means that classification models can provide valuable results. Machine learning models provide great results in terms of performance for the classification of activity types. The performance of all models on input set 3 was either similar to or better than their performance on input set 2. This suggests that the reduced data in input set 3 still captured the essential patterns needed for prediction, while the data in input set 2 may lack some significant features. The use of input set 1 required more processing power, and this has a direct impact on the hardware specifications of a wearable. It is not necessary to process all the data from the wearable sensors; the same results can be obtained with a low-cost wearable. The results from both the data visualization and the machine learning comparison indicate the promising potential of a lightweight system which can assess different activities performed by an elderly individual during the course of a day in order to detect frailty. Activity detection can be applied to daily activities and provide energy expenditure insights. Energy expenditure is considered a baseline for physical activity, and it can be used in conjunction with other information to quantify early signs of frailty in the elderly. It is safe to assert that using a combination of an accelerometer, an orientation sensor, and heart rate data is sufficient to distinguish between different daily activities. The comparison between the initial input data and the lighter datasets derived from it gives another valuable insight: it is not necessary to transfer all the data from a smartwatch to a data processor to extract valuable information about activity types. Effective results can be obtained using the vector magnitude of the accelerations and the orientation sensor quaternions. More research is needed to overcome the limitations of the prototype. The most concerning of these is the subjectivity and variability of the collected data. Different people may exhibit different patterns of movement during fast walk, and more analysis is needed to see how data varies among individuals with different characteristics (e.g., age, weight, and height). The nature of the data collection is another limitation: to obtain big datasets requires a lot of physical activity in different recording sessions, and this is a challenge. The frailty-detection process should also involve the measurement and analysis of physiological parameters. Due to technical and financial limitations, our prototype only records heart rate values. Future work should also aim to include other parameters, such as respiratory rate, blood pressure, and blood oxygenation, to detect whether there are connections between these and early signs of frailty. Collecting data on the movement patterns of individuals, especially in the context of physical activity, may involve capturing sensitive information that these individuals might not be comfortable sharing. These could include details about their health conditions, disabilities, or other personal details that they may not want to disclose or have used against them. Moreover, there is a risk that the data collected through these prototypes could be vulnerable to misuse or unauthorized access. If the collected data are not handled properly, they could be exploited by malicious entities for purposes such as identity theft, targeted advertising, or discriminatory practices. Therefore, it is crucial to address the ethical considerations surrounding data collection in prototype technologies. This includes implementing robust security measures, obtaining informed consent from participants, and ensuring that data are anonymized and used only for their intended purposes.