**Kee S. Moon 1,\*, Sung Q Lee 2,\*, Yusuf Ozturk 3, Apoorva Gaidhani <sup>1</sup> and Jeremiah A. Cox <sup>1</sup>**


Received: 31 August 2019; Accepted: 13 November 2019; Published: 18 November 2019

**Abstract:** Gait signifies the walking pattern of an individual. It may be normal or abnormal, depending on the health condition of the individual. This paper considers the development of a gait sensor network system that uses a pair of wireless inertial measurement unit (IMU) sensors to monitor the gait cycle of a user. The sensor information is used for determining the normality of movement of the leg. The sensor system places the IMU sensors on one of the legs to extract the three-dimensional angular motions of the hip and knee joints while walking. The wearable sensor is custom-made at San Diego State University with wireless data transmission capability. The system enables the user to collect gait data at any site, including in a non-laboratory environment. The paper also presents the mathematical calculations to decompose movements experienced by a pair of IMUs into individual and relative three directional hip and knee joint motions. Further, a new approach of gait pattern classification based on the phase difference angles between hip and knee joints is presented. The experimental results show a potential application of the classification method in the areas of smart detection of abnormal gait patterns.

**Keywords:** gait analysis; wearable sensors; inertial measurement unit; human kinematics; phase difference angle
