# IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model
## 5 IoT System Performance
### Data Collection and Activity Recognition
With the overall subject range approach, it is difficult to discriminate and recognize a particular activity as the degree of overlap is quite high. **Case 2:** In case 2, and as presented in Figure 6, the subject was instructed to perform LOB (1 min), static state (1 min), and LOS (1 min). The analysis portrays a 66% recognition accuracy of LOB activity with a 26% overlap with LM, whilst 8% accounts for unrecognized activity. Again, the static state of a subject is 100% accurate. LOS is 87% accurately recognized whilst 13% of the activity is unrecognized, but no overlap is observed with other activities. In comparison to the overall subject range, LOB activity overlaps with four different activities, i.e., LM, SLTS, and LOS. However, LOS overlaps with three different activities, i.e., LTU, SLTS, and LOB. From the above two cases' analyses, it is evident that personalization is a better approach for the recognition of a subject's activity movement behavior. As a result, it represents the first level of the recognition process that reduces the overlap among the activities in comparison to the overall subject range. The second level of improving the recognition process is based on looking at the logical switching of the activity transition rules and the number of occurrences of a particular activity performed or overlapped. This would help in the correction of any misrecognized activity behavior observed during the past one minute. The overall summary of the findings of case 1 and case 2 is represented in Table 1.  
Results show that for case 1, the subject performed the LTU activity 13 times, and overlapped with LOS twice. Since the LOS activity was performed in a lying position compared to LTU, which was performed in standing position, overlapping two times clearly shows that the activity was misrecognized. Therefore, with the possible implementation of this logic, the subject's LTU activity can be corrected and hence recognized with 100% accuracy. It has also been observed that the swinging leg to a side activity was performed 11 times, and overlapped with the leg movement activity four times. It is evident that  
Figure 5: Long-term group-based activity performed (LTU, static and SLTS) recognition accuracy comparative analysis with respect to personalized and overall subject ranges. Figure 6: Long-term group-based activity performed (LOB, static and LOS) recognition accuracy comparative analysis with respect to personalized and overall subject ranges. the leg movement activity was misrecognized as it was performed in sitting position and quickly switching over to the swinging leg to a side activity performed in standing position is not feasible. Hence, the misrecognition of the swinging leg to a side activity can be corrected. In case 2, the subject performed the LOB activity 11 times, and overlapped with the leg movement activity 4 times. As the LOB activity was performed in lying position, the leg movement activity was misrecognized as the activity was performed in sitting position. Moreover, sudden transition from lying to sitting may result in the capturing of erroneous movement transition data, instead of a particular activity's movement data. Therefore, the LOB activity is recognized with 100% precision. In another scenario, LOS activity was recognized 13 times whilst the activity was unrecognized twice. As a result, LOS activity was recognized with 100% accuracy. This shows that personalization and looking at the activity movement behavior for the last minute based on logical switching and the occurrences of the activity movement types has considerably improved the recognition accuracy. The next section discusses the IoT system's performance for possible scenarios of architectural implementations which takes into consideration the available network functions, the information transparency, and wireless sensor lifetime.