Inertial measurement units are commonly used to estimate the orientation of sections of sections of human body in inertial navigation systems. Most of the algorithms used for orientation estimation are computationally expensive and it is difficult to implement them in real-time embedded systems with restricted capabilities. This paper discusses a computationally inexpensive orientation estimation algorithm (Gyro Integration-Based Orientation Filter—GIOF) that is used to estimate the forward and backward swing angle of the thigh (thigh angle) for a vision impaired navigation aid. The algorithm fuses the accelerometer and gyroscope readings to derive the single dimension orientation in such a way that the orientation is corrected using the accelerometer reading when it reads gravity only or otherwise integrate the gyro reading to estimate the orientation. This strategy was used to reduce the drift caused by the gyro integration. The thigh angle estimated by GIOF was compared against the Vicon Optical Motion Capture System and reported a mean correlation of 99.58% for 374 walking trials with a standard deviation of 0.34%. The Root Mean Square Error (RMSE) of the thigh angle estimated by GIOF compared with Vicon measurement was 1.8477°. The computation time on an 8-bit microcontroller running at 8 MHz for GIOF is about a half of that of Complementary Filter implementation. Although GIOF was only implemented and tested for estimating pitch of the IMU, it can be easily extended into 2D to estimate both pitch and roll.

The ability to accurately perform human gait evaluation is critical for orthopedic foot and ankle surgeons in tracking the recovery process of their patients. The assessment of gait in an objective and accurate manner can lead to improvement in diagnoses, treatments, and recovery. Currently, visual inspection is the most common clinical method for evaluating the gait, but this method can be subjective and inaccurate. The aim of this study is to evaluate the foot drop condition in an accurate and clinically applicable manner. The gait data were collected from 56 patients suffering from foot drop with L5 origin gathered via a system based on inertial measurement unit sensors at different stages of surgical treatment. Various machine learning (ML) algorithms were applied to categorize the data into specific groups associated with the recovery stages. The results revealed that the random forest algorithm performed best out of the selected ML algorithms, with an overall 84.89% classification accuracy and 0.3785 mean absolute error for regression.

Foot drop is one of the common gait abnormalities which are difficult to detect, diagnose and evaluate. While various gait monitoring systems are available, many are computationally expensive and difficult to implement outside laboratory environments. This study introduces an in-house designed system based on inertial measurement units to capture the gait symptoms, specifically in the case of foot drop symptoms. The system specification and communication results, as well as filtering methods are discussed. Also, the pitch angle of thigh, shank and foot from a subject with no reported foot problem have been compared (gathered from identical equipment under similar conditions) to the same angle from a foot drop subject.

Foot drop is a frequent gait disorder which is challenging to identify. Currently, subjective methods are being applied for detection of foot drop in clinical applications. This research introduces an objective procedure based on Inertial Measurement Units to monitor the gait symptoms and distinguish foot drop abnormalities. The proposed system was used to capture gait events from two groups, subjects without a reported gait abnormality and those who had presented with foot drop before lumbar spine surgery. The two groups were monitored during "normal-style walking" trails and the range of movement was compared. Also, frequency components of gait patterns were extracted to analyse the gait characteristics.