Assessing the frailty of older people quantitatively is critical to prevent potential accidents and to ensure their well-being. The older people with high frailty score are at the risk of fall, which increases the rate of hospitalization and reducing the number of independent activities carried out. The conventional clinical tools used for frailty assessment are subjective, qualitative and are prone to human error. The balance assessment, activity of daily living (ADL) and gait analysis are practiced as clinical and quantitative tools for risk of fall and frailty assessments. An objective approach to classify the frailty levels using ADL is proposed. The pick up an object from floor as an ADL is deployed to differentiate the signal patterns obtained through inertial measurement unit (IMU) for frail and non-frail subjects. The data from single inertial unit mounted on pelvis is analyzed. The experimental work is carried out on three groups of healthy/control, frail and non-frail subjects. The various signal attributes are used to classify the frailty quantitatively using IMU data and machine learning methods. The results demonstrate that frail subjects have clear irregularities in their signal trajectories. Using the proposed algorithm two classes of frailty (non-frail and frail) are identified objectively. The study demonstrates the potential of deploying IMU for advanced classification of frailty levels in older people.