Smart clothes designed for healthcare applications generally enable the monitoring of heart rate, respiration rate, electrocardiograph and temperature. In order to gather information about the user's health accurately, sensor placement at the right location on a smart garment is essential. In this study, to detect the entrail and muscle disease of a wearer, suitable sensor location on a smart garment was determined using acceleration measurements from different parts of the body. An accelerometer was placed on different locations of the garment to measure the respiration rate, heart rate and muscle tremor in order to determine any respiration difficulty, heart trouble and some mental illnesses. More than 700 measurements were acquired from different carriers with different age groups, different gender groups and variable body postures, such as in a sleeping position, sitting on a chair and running. Additionally, some significant features were extracted from the acquired data by using fast Fourier transform, wavelet and bispectral analysis, and they were categorized with different rule and pattern classification methods in order to detect over- and under-range heart rates and respiration rate. This study represents the pre-cursor of the healthcare smart clothing system design based on signal analysis and data classification methods.