Real-Time Gait Phase Detection Using Wearable Sensors for Transtibial Prosthesis Based on a kNN Algorithm.

Those with disabilities who have lost their legs must use a prosthesis to walk. However, traditional prostheses have the disadvantage of being unable to move and support the human gait because there are no mechanisms or algorithms to control them. This makes it difficult for the wearer to walk. To overcome this problem, we developed an insole device with a wearable sensor for real-time gait phase detection based on the kNN (k-nearest neighbor) algorithm for prosthetic control. The kNN algorithm is used with the raw data obtained from the pressure sensors in the insole to predict seven walking phases, i.e., stand, heel strike, foot flat, midstance, heel off, toe-off, and swing. As a result, the predictive decision in each gait cycle to control the ankle movement of the transtibial prosthesis improves with each walk. The results in this study can provide 81.43% accuracy for gait phase detection, and can control the transtibial prosthetic effectively at the maximum walking speed of 6 km/h. Moreover, this insole device is small, lightweight and unaffected by the physical factors of the wearer.

Morphometric measurement of the proximal tibia to design the tibial component of total knee arthroplasty for the Thai population.

PURPOSE: This study evaluates the morphology of the Thai proximal tibia based on three-dimensional (3D) models to design the tibial component. METHODS: The 3D models of 480 tibias were created using reverse engineering techniques from computed tomography imaging data obtained from 240 volunteers (120 males, 120 females; range 20-50 years). Based on 3D measurements, a digital ruler was used to measure the distance between the triangular points of the models. The morphometric parameters consisted of mediolateral length (ML), anteroposterior width (AP), medial anteroposterior width (MAP), lateral anteroposterior width (LAP), central to a medial length (CM), central to a lateral length (CL), medial anterior radius (MAR), lateral anterior radius (LAR), and tibial aspect ratio (AR). An independent t-test was performed for gender differences, and K-means clustering was used to find the optimum sizes of the tibial component with a correlation between ML length and AP width in Thai people. RESULTS: The average morphometric parameters of Thai proximal tibia, namely ML, AP, MAP, LAP, CM, and CL, were as follows: 72.52 ± 5.94 mm, 46.36 ± 3.84 mm, 49.22 ± 3.62 mm, 43.59 ± 4.05 mm, 14.29 ± 2.72 mm, and 15.28 ± 2.99 mm, respectively. The average of MAR, LAR, and AR was 24.43 ± 2.11 mm, 21.52 ± 2.00 mm, and 1.57 ± 0.08, respectively. All morphometric parameters in males were significantly higher than those of females. There was a difference between the Thai proximal tibia and other nationalities and a mismatch between the size of the commercial tibial component and the Thai knee. Using K-means clustering analysis, the recommended number of ML and AP is seven sizes for the practical design of tibial components to cover the Thai anatomy. CONCLUSION: The design of the tibial component should be recommended to cover the anatomy of the Thai population. These data provide essential information for the specific design of Thai knee prostheses.

Craniometric study of Thai skull based on three-dimensional computed tomography (CT) data.

The present study revealed an advanced method using data obtained from three-dimensional computed tomography (3D CT) to evaluate the craniometric data of the Thai population. Ninty-one Thai cadaveric dry skulls from the Faculty of Medicine, Khon Kaen University were investigated in the present study. It enabled the authors to assess the three-dimensional anatomical landmarks in digital format without physical measurements. The results have revealed that the craniometric data of Thai males were larger than Thai females with a statistical significant difference, especially, the maximum cranial length, basion-bregma height, nasion-basion length, nasion-bregma length and bizygomatic breadth parameters (p << 0.001). In addition, the craniometric data based on Thai skulls of the people in the northeast region was different from the people in the central region. Furthermore, the linear regression equations obtained from the pairwise parameter, it is useful to predict the craniometric parameters in forensic medicine.