# Validation of a Wearable IMU System for Gait Analysis: Protocol and Application to a New System
## 1 Introduction  
Gait Analysis (GA) is one functional test used to determine the motor capability of subjects for clinical evaluation [1]. It is currently carried out with advanced technologies including a multifactorial setup: an optoelectronic motion capture system for body kinematics collection, force platforms for ground reaction force measurement, and wireless electromyography (EMG) systems for recording themuscles activations. This GA setup is the gold standard reference technology following its proven and consolidated high accuracy. However, GA is complex and expensive in time and equipment, only few steps in a path can be analyzed, and it requires a skilled operator to perform it. It is true that to increase the number of steps to be analyzed within a small area, instrumented treadmills with an optoelectronics system could be used, taking advantage of the stationary subject's position and static monitoring equipment's position. However, it should be emphasized that on a treadmill, the subject's walking cycle differs from the natural one, resulting in decreased stride length (4%). In addition, it increases the step cadence (6%) and this results in a shorter walking cycle and consequently some variations for the decreased stance (7%) and the increased swing phases (5%). It also increases the step width related to balance (22%) [2; 3; 4]. Because of these differences, the subjects should be adequately trained for about 1 h to be confident to walk over the treadmill [5] prior to testing. Thanks to the availability of miniaturized wearable Inertial Measurement Units (IMUs), embedding 3D accelerometers, gyroscopes, and magnetometers, a new generation of systems may offer new opportunities for the functional assessment of motor functions for medicine, sport, and ergonomics. Some of them are based on multisensory platforms (e.g., Xsense, Yost, etc.) [6; 7], while others prefer a single IMU approach to collect kinematic data [8; 9; 10]. These systems are lightweight, very small, and inexpensive. New textile technologies allow researchers to integrate connection with mesh wire, producing sensorized clothes [11; 12; 13]. The integration between clothes and sensors is also helped by the development of flexible devices. It is possible to use the IMUs both as a single device and as a multisensor cooperative system. The Wearable Health Systems (WHS) are able to measure, monitor and evaluate all parameters of biomedical interest from the human body [14]. This solution allows researchers to better identify activity or events. Relative to human walking, using these systems, it is possible to monitor events as the fall of a subject, or also to track in real time the trajectory of anatomical segments in total body configuration and to estimate the kinematic parameters of the gait cycle [8; 9; 10; 11; 12; 15; 16; 17; 18; 19; 20; 21; 22]. The system could be very useful for patients, caregivers, and even for clinicians to support their clinical decision, as long as it is: (a) a user-friendly system; (b) suitable to be used outdoors, in real life, and not only in the laboratory; (c) capable of monitoring the subject in natural conditions for a long period of time, without altering the natural and normal execution of movements and activities; and (d) without a very complicated calibration process. In fact, a system with this kind of flexibility and adaptability could objectively, quantitatively, and qualitatively measure the gait and mobility of patients with orthopedic or neurologic impairments that affect motor control and support clinicians with new data crucial for decision-making. A solution could be implemented by IMUs. In our previous research [23; 24], over the classical pendulum model, we proposed a new and original kinematic model using a harmonic oscillator to study spatiotemporal parameters of the walking cycle by only one accelerometer without the drift correction and integration of acceleration data. This model is used for data analysis using raw data acquired by a single IMU next to the center of mass.