# IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model
## 5 IoT System Performance
### Long-Term Data Presentation and Cloud Role  
This section presents the long-term data presentation of different activity movements performed by a young healthy subject during the day at the Cloud level. The subject placed the wearable device on his ankle for the whole day and was instructed to perform the post-operative hip fracture rehabilitation activities. This was the preliminary step in relating the subject activity movement recognition with the rehabilitation model discussed in Section 3. The activity movement recognition data collected at the Cloud level were then analyzed and presented. Figure 10 presents the sample representation of the real-time activity movement recognition performed by a subject during a day. According to our proposed approach, each activity is recognized every 4 s. Therefore, the data visualization presented depicts the different types of rehabilitation activity movement performed by the subject. For instance, the subject was in static state three times, whilst the subject performed the leg movement activity twice. Figure 10: Sample representation of the real-time activity movement recognition performed by a subject during a day. Figure 9: Analysis of sensor wearable energy consumption when processing is taking place at the sensor wearable and gateway edge. Marginal difference in energy consumption due to the available redundancy in the commercial device used. In support of the sample representation, an overall summary representation of the daily frequency practice of the activity performed by a subject during the day is presented in Figure 11. The numbers related to daily frequency practice refers to the number of each repetition performed every 4 s by a patient for a particular activity. For instance, the patient was doing slow walking activity 10 times of 4 s recognition. Results show that the subject was static most of the time. The ambulatory movements, namely slow and fast walking, were performed 9 and 10 times, respectively. Hip fracture-related activities such as LM, LM, LTU, SLTS, LOB, and LOS were performed 6, 4, 5, 15, and 13 times, respectively. UA refers to the unrecognized activity and 10 times the activity was unrecognized. This is because the patient might be performing other ADLs as part of their daily routine and not related to the rehabilitation activity movements or some of the activity movements overlapping with each other. This type of information is useful for the physiotherapist and medical professionals to observe how the patient is performing on an hourly, daily, weekly, or monthly basis depending on one's requirements. Moreover, such information is of great significance for mapping with the proposed rehabilitation model. This would help in determining the stage at which the patient is at and their progression level so that a follow up can be performed when required and in case of an emergency. Since this was a preliminary step in portraying the overall summary of the patient activity movement behavior, further research is required to investigate how often (hourly, daily, weekly, or monthly) the physiotherapist or medical professionals prefer the subject's activity movement behavior information to be presented. Secondly, we must investigate the rules to be set while relating the subject's activity movement behavior with the proposed model. Thirdly, we must identify the key performance indicators for the patient mobility improvements and associate the key measurements required. This could help the overall process to be automated and aid the patient and medical professionals take the necessarily follow-up actions for progressing the healing process. While the above work has clarified a number of design challenges, future works may involve the following areas:  
Figure 11: Overall summary sample of the daily frequency practice of the activity movement performed by a young healthy subject.