Walking Motion Trajectory of Hip Powered Orthotic Device Using Quintic Polynomial Equation

In lower limb exoskeleton system walking motion profile generation, cubic polynomial is commonly used to generate smooth walking profile on flexion angle, velocity and acceleration data of three joint movements (ankle, knee and hip joints). However, cubic polynomial does not closely matched human motion. For this reason, a higher-order-polynomial i.e. quintic polynomial is proposed to gene- rate walking motion profile. Error analysis was conducted to measure how closely quintic polynomial could represent human walking motion profile. Result shows that quantic polynomial could closely represent human walking trajectory with maximum RMS error of 0.2607rad occurred during mid-swing phase

Child Value and Gender Preference Among Konjo Tribe: A Rapid Ethnography Study in Bulukumba Rural Coast Indonesia

Parents' perceptions and expectations for their children are different in variousculture; it depends on how families assess the presence of children in the family and their preference for specific gender. This research aims to assess the perception of children value and gender preferences in Konjo Tribeof Bulukumba Rural Coast, in Indonesia. Using compressed design approach, through rapid etnography assessment. The subject consist of 30 informants, withspesific various couple, public figure, and health workers and assitant Family Planning Staff. The results show that value of children forKonjoTribe community divide into, 1) children are treasure, 2) children are symbol of pride, 3) children are special and precious, 4) children are symbol of family wholeness. While parents preferences of spesific gender in a single family of Konjo Tibe shows different meaning for the existance of sons and daughters, they do more prefer to have son rather than daughter for their first born. It means that in Konjo Tribe, the population will be enlivened with males more than females, considering man as a successor of their generation

A New Data Glove Approach For Malaysian Sign Language Detection

A normal human being sees, listens, and reacts to his/her surroundings. There are some individuals who do not have this important blessing. Such individuals, mainly deaf and dumb, depend on communication via sign language to interact with others. However, communication with ordinary individuals is a major concern for them since not everyone can comprehend their sign language. Furthermore, this will cause a problem for the deaf and dumb communities to interact with others, particularly when they attempt to involve with educational, social and work environments. In this research, the objectives are to develop a sign language translation system in order to assist the hearing or speech impaired people to communicate with normal people, and also to test the accuracy of the system in interpreting the sign language. As a first step, the best method in gesture recognition was chosen after reviewing previous researches. The configuration of the data glove includes 10 tilt sensors to capture the finger flexion, an accelerometer for recognizing the motion of the hand, a microcontroller and Bluetooth module to send the interpreted information to a mobile phone. Firstly the performance of the tilt sensor was tested. Then after assembling all connections, the accuracy of the data glove in translating some selected alphabets, numbers and words from Malaysian Sign Language is performed. The result for the first experiment shows that tilt sensor need to be tilted more than 85 degree to successfully change the digital state. For the accuracy of 4 individuals who tested this device, total average accuracy for translating alphabets is 95%, numbers is 93.33% and gestures is 78.33%. The average accuracy of data glove for translating all type of gestures is 89%. This fusion of tilt sensors and accelerometer could be improved in the future by adding more training and test data as well as underlying frameworks such as Hidden Markov Model

Analysis Of Spinal Electromyography Signal When Lifting An Object

Lifting and swinging are daily activities that human do using the spine.Furthermore,spine provides support during standing and walking.Therefore,it is very important in everyday activities and it will be inconvenient when it is injured.Technology has provided ways to machine and human integration in helping or supporting people in their daily tasks.To make this integration successful, machines or robots need to understand the human muscle activity.To do so,electromyography (EMG) a bio signal record the electricity generated by muscle was implemented.However,the signal often influenced by the unwanted noise.In this paper,the MVC normalization method is applied to determine the spinal EMG signal on lumbar multifidus muscle when lifting an object.In order to analyze the identity of spinal EMG signal,two statistical analyses are done;1) ANOVA analysis and 2)Boxplot analysis.The signal will go through 8th order Gaussian function or Exponential Weight Moving Average Filter before being analysed.Results show that Exponential Weight Moving Average Filter gives more consistent value compared to 8th order Gaussian function which is 0.0428V RMSE based on linear fitting done from the maximum amplitude gather from the boxplot analysis done