Recognizing Complex Human Activities using Hybrid Feature Selections based on an Accelerometer Sensor

Wearable sensor technology is evolving in parallel with the demand for human activity monitoring applications. According to World Health Organization (WHO), the percentage of health problems occurring in the world population, such as diabetes, heart problem, and high blood pressure rapidly increases from year-to-year. Hence, regular exercise, at least twice a week, is encouraged for everyone, especially for adults and the elderly. An accelerometer sensor is preferable, due to privacy concerns and the low cost of installation. It is embedded within smartphones to monitor the amount of physical activity performed. One of the limitations of the various classifications is to deal with the large dimension of the feature space. Practically speaking, a large amount of memory space is demanded along with high processor performance to process a large number of features. Hence, the dimension of the features is required to be minimized by selecting the most relevant feature before it is classified. In order to tackle this issue, the hybrid feature selection using Relief-f and differential evolution is proposed. The public domain activity dataset from Physical Activity for Ageing People (PAMAP2) is used in the experimentation to identify the quality of the proposed method. Our experimental results show outstanding performance to recognize different types of physical activities with a minimum number of features. Subsequently, our findings indicate that the wrist is the best sensor placement to recognize the different types of human activity. The performance of our work also been compared with several state-of-the-art of features for selection algorithms

Design of a Low Voltage Class AB Variable Gain Amplifier (VGA)

A variable gain amplifier (VGA) is one of the most significant component in many applications such as analog to digital converter (ADC). In communication receiver, VGA is typically employed in a feedback loop to realize an automatic gain control (AGC), to provide constant signal power to baseband analog-to-digital converter (ADC) for unpredictable received signal strengths. Gain range, power consumption and bandwidth of ADC are strongly influenced by the performance of operational amplifier. VGA is the key element for amplifying process in ADC. However, current class AB VGA is experiencing the limit of bandwidth, which is not suitable for high speed automatic gain control AGC. In order to overcome these limitations a high linearity and wide bandwidth of VGA is indispensable. The aim of this research is to get higher gain and larger bandwidth for VGA. In this research, a low cost, low power voltage and wide bandwidth class AB VGA is designed to mitigate this constraint. Superiority of the proposed VGA has been confirmed by circuit simulation using CEDEC 0.18-μm CMOS process with the help of tools from Mentor Graphics in designing a 100-MHz VGA under 1V supply voltage draining total static power consumption less than 125uW. The results show that the circuit is able to work with high linearity and wide bandwidth by varying Rf and Rs. Therefore, the frequency response (Gain) and the wide bandwidth of this class AB VGA is better than previously reported class AB VGA. Consequently, this modified class AB VGA is appropriate for high speed applications