Stroke is a major reason for physical immobility and death. For effective treatment of stroke, early diagnosis and aggressive medication in the form of thrombolytic drugs is shown to be essential. In order to provide proper care, the patient should be kept under continuous monitoring during the first few hours after subjecting thrombolytic drugs and based on the response of the patient to the medication, line of treatment should be changed. In our previous work [1], we have shown the proof of principle by monitoring the motor activity of the stroke patient using accelerometer fitted on patient's arms. Based on preliminary analysis, we proposed methods using resultant acceleration signal and showed its effectiveness in predicting National Institute of Health Stroke Scale (NIHSS) stroke index. In this paper, novel technique based on cross-correlation of accelerometer values along different axes is developed for predicting the NIHSS index. An overall increase in prediction accuracy by over 7% compared to the earlier method is obtained. A multi-class support vector machine (SVM) classifier for cross correlation features is also designed and an overall prediction accuracy of 93% is achieved.