Real-Time Gait Analysis Algorithm for Patient Activity Detection to Understand and Respond to the Movements

University essay from Blekinge Tekniska Högskola/Sektionen för datavetenskap och kommunikation; Blekinge Tekniska Högskola/Sektionen för datavetenskap och kommunikation

Abstract: Context: Most of the patients suffering from any neurological disorder pose ambulatory disturbance at any stage of disease which may result in falling without showing any warning sign and every patient is different from another. So there is a need to develop a mechanism to detect shaky motion. Objectives: The major objectives are: (i) To check different gait parameters in walking disorders using Shimmer platform (R). (ii) Wearing SHIMMER wireless sensors on hip, waist and chest, to check which one is the most suitable. (iii) To draw effective conclusion/results based on calibrated data in real time and offline processing in EyesWeb/Matlab.To develop an effective mechanism/algorithm for security warning and activating alarm systems. Methods: Our thesis project is related to analyze real-time gait of the patient suffering from Parkinson's disease for actively responding to the shaky movements. Based on real world data, we have developed a mechanism to monitor a real time gait analysis algorithm to detect any gait deviation. This algorithm is efficient, sensitive to detect miner deviation and not hard coded i.e. user can set Sampling Rate & Threshold values to analyze motion. Researchers can directly use this algorithm in their study without need to implement themselves. It works on pre-calculated threshold values while initial sampling rate is set to 100MHz. Results: Accelerometers putting on the chest shows high unnecessary acceleration during fall, suggest putting on waist position. Also, if a patient initiates steps with energy, his/her gait may become more stable as shown in the conscious gait. Results show that after DBS surgical procedure, the patient still experiences postural instability with fall. So it is evident to show that such patients may have reduced cognition even after surgery. Another finding is that such patients may lean left or right during turning. Conclusions: We have presented a real time gait analysis algorithm, capable of detecting the motion of the patient with PD to actively respond to the shakier motion setting threshold values. Our proposed algorithm is easy to implement, reusable and can affectively generate healthcare alarms. Additionally, this system might be used by other researchers without the need to implement by themselves. The proposed method is sensitive to detect fall therefore objectively can be used for fall risk assessment as well .The same algorithm with minor modifications can be used for seizure detection in other disorders mainly epileptic seizers to alert health providers for emergency.

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