This report summarizes the existing literature on the use of technology for the assessment of freezing of gait (FOG) as well as the use of technology to provide insights into the mechanisms of FOG in people with Parkinson's disease. Specifically, this work was carried out for the 3rd International Workshop on Freezing of Gait in Jerusalem in 2023. This review focuses on the most used technologies to quantitatively assess FOG in a laboratory environment and describes the technologies that hold promise for assessing FOG in daily life. Examples of implementation of machine learning algorithms are provided as well as algorithmic biases. Lastly, a standardized assessment using inertial measurement units during a clinical protocol is proposed and a 5-year outlook is discussed. We anticipate this review will help move the field forward in the coming years.

Background
Manual annotation of initial contact (IC) and end contact (EC) is a time consuming process. There are currently no robust techniques available to automate this process for Parkinson's disease (PD) patients with freezing of gait (FOG).

Objective
To determine the validity of a data-driven approach for automated gait event detection.

Methods
15 freezers were asked to complete several straight-line and 360 degree turning trials in a 3D gait laboratory during the off-period of their medication cycle. Trials that contained a freezing episode were indicated as freezing trials (FOG) and trials without a freezing episode were termed as functional gait (FG). Furthermore, the highly varied gait data between onset and termination of a FOG episode was excluded. A Temporal Convolutional Neural network (TCN) was trained end-to-end with lower extremity kinematics. A Bland-Altman analysis was performed to evaluate the agreement between the results of the proposed model and the manual annotations.

Results
For FOG-trials, F1 scores of 0.995 and 0.992 were obtained for IC and EC, respectively. For FG-trials, F1 scores of 0.997 and 0.999 were obtained for IC and EC, respectively. The Bland-Altman plots indicated excellent timing agreement, with on average 39% and 47% of the model predictions occurring within 10 ms from the manual annotations for FOG-trials and FG-trials, respectively.

Significance
These results indicate that our data-driven approach for detecting gait events in PD patients with FOG is sufficiently accurate and reliable for clinical applications.