METHODS: Twenty stroke patients from conventional rehabilitation (CR) (n = 10) and RR (n = 10) groups were recruited through a purposive sampling method. Patients in the CR group received a two-hour session of a five-day-a-week home-based CR program for 4 weeks. Patients in the RR group received a five-day-a-week of an hour combined physiotherapy and occupational therapy session and a one-hour robotic therapy session using the HAL® Cyberdyne lower-limb, for 4 weeks. The mid-thigh circumferences of both limbs were measured on day 1 (baseline), week 2 and week 4 of rehabilitation program.
RESULTS: The results revealed no statistically significant difference in the mid-thigh circumferences between the paretic (F1.05,9.44 = 1.96, p = 0.20), and the normal (F1.05,9.44 = 1.96, p = 0.20) sides in the CR group (n = 10). For the comparison between the paretic and normal sides in the RR group (n = 10), the paretic mid-thigh circumferences revealed significant time effect results (F2,18 = 11.91, p = 0.001), which were due to changes between baseline and week 2, and baseline and week 4 measurements. Interestingly, the normal mid-thigh circumferences also revealed a significant time effect (F2,18 = 6.56, p = 0.007), which is due to changes between baseline and week 4. One-way analysis of variance was employed to compare the mean average between groups due to the difference in the baseline measurements of the mid-thigh circumferences between the paretic side of the CR and the RR groups. With this adjustment, the average means mid-thigh circumferences after 4 weeks of therapy were shown to be significantly different between the CR and RR groups (F1,18 = 12.49, p = 0.02).
CONCLUSION: Significant increments in the mid-thigh circumferences following RR were seen in the paretic limbs of stroke patients. Hence, this study may provide some insights into further potential research related to the benefits of RR in stroke patients.
METHODS: In this study, the frequency and causes of line of sight issues is assessed using recordings of Navigation probe locations and its synchronised video recordings.
RESULTS: The mentioned experiment conducted for a series of 15 neurosurgical operations. This issue occured in all these surgeries except one. Maximum duration of issue presisting reached up to 56% of the navigation usage time.
CONCLUSIONS: The arrangment of staff and equipment is a key factor in avoiding this issue.
OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.
RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.
CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.
OBJECTIVES: This paper discusses RISAPI of our original work in the field, which shows how probabilistic planning and system theory algorithms in workplace robotic systems that work with people can allow for that reasoning using a security robot system. The problem is a general way as an incomplete knowledge 2-player game.
RESULTS: In this general framework, the various hypotheses and these contribute to thrilling and complex robot behavior through real-time interaction, which transforms actual human subjects into a spectrum of production systems, robots, and care facilities.
CONCLUSION: The models of the internal human situation, in which robots can be designed efficiently, are limited, and achieve optimal computational intractability in large, high-dimensional spaces. To achieve this, versatile, lightweight portrayals of the human inner state and modern algorithms offer great hope for reasoning.
OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection.
RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs.
CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.
Material and Methods: Drilling processes using three brands of drills attached to a robotic arm were compared in terms of thrust force, vibration, noise level, speed deviation, and temperature. A standardised experimental setup was constructed, and measurement data were analysed statistically. Identical artificial bones were drilled 10 times with each drill.
Results: Thrust force measurements, which varied through the cortex and medulla, showed expressive differences for each drill for maximum and mean values (p<0.001). Meaningful differences were obtained for mean vibration values and noise level (p<0.001). Speed variation measurements in drilling showed conspicuous differences with confident statistics (p<0.001). Induced temperature values were measured statistically for Drill 1, Drill 2, and Drill 3 as 78.38±11.49°C, 78.11±7.79°C, and 89.77±7.79°C, respectively.
Conclusion: Thrust force and drill bit temperature were strongly correlated for each drill. Vibration values and noise level, which also had an influential relationship, were in the acceptable range for all experiments. Both thrust force and speed deviation information could be used to detect the drill bit status in the bone while drilling.
METHODS: On the basis of a series of bone milling experiments with commercial artificial bones, an artificial neural network force model is developed to estimate the milling force of different bone densities as a function of the milling feed rate and spindle speed. The model estimations are used to identify the bone density at the cutting zone by comparing the actual milling force with the estimated one.
RESULTS: The verification experiments indicate the ability of the proposed method to distinguish between one cortical and two cancellous bone densities.
CONCLUSIONS: The significance of the proposed method is that it can be used to discriminate a set of different bone density layers for a range of the milling feed rate and spindle speed.