*Article* **Recognition of Gait Phases with a Single Knee Electrogoniometer: A Deep Learning Approach**

### **Francesco Di Nardo \*, Christian Morbidoni \*, Alessandro Cucchiarelli and Sandro Fioretti**

Department of Information Engineering, Università Politecnica delle Marche, 60100 Ancona, Italy; a.cucchiarelli@univpm.it (A.C.); s.fioretti@staff.univpm.it (S.F.)

**\*** Correspondence: f.dinardo@staff.univpm.it (F.D.N.); c.morbidoni@univpm.it (C.M.); Tel.: +39-071-220-4838 (F.D.N.); +39-071-220-4830 (C.M.)

Received: 27 January 2020; Accepted: 18 February 2020; Published: 20 February 2020

**Abstract:** Artificial neural networks were satisfactorily implemented for assessing gait events from different walking data. This study aims to propose a novel approach for recognizing gait phases and events, based on deep-learning analysis of only sagittal knee-joint angle measured by a single electrogoniometer per leg. Promising classification/prediction performances have been previously achieved by surface-EMG studies; thus, a further aim is to test if adding electrogoniometer data could improve classification performances of state-of-the-art methods. Gait data are measured in about 10,000 strides from 23 healthy adults, during ground walking. A multi-layer perceptron model is implemented, composed of three hidden layers and a one-dimensional output. Classification/prediction accuracy is tested vs. ground truth represented by foot–floor-contact signals, through samples acquired from subjects not seen during training phase. Average classification-accuracy of 90.6 ± 2.9% and mean absolute value (MAE) of 29.4 ± 13.7 and 99.5 ± 28.9 ms in assessing heel-strike and toe-off timing are achieved in unseen subjects. Improvement of classification-accuracy (four points) and reduction of MAE (at least 35%) are achieved when knee-angle data are used to enhance sEMG-data prediction. Comparison of the two approaches shows as the reduction of set-up complexity implies a worsening of mainly toe-off prediction. Thus, the present electrogoniometer approach is particularly suitable for the classification tasks where only heel-strike event is involved, such as stride recognition, stride-time computation, and identification of toe walking.

**Keywords:** knee angle; deep learning; neural networks; gait-phase classification; electrogoniometer; EMG sensors; walking; gait-event detection
