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Drop foot is a syndrome characterised by the difficulty in lifting the front part of the foot, caused by the weakness or disability of muscles responsible for foot dorsiflexion. Ankle Foot Orthosis (AFO) is an orthopaedic support used in drop foot patients to support and maintain the foot and ankle in the proper position during movement, improving the gait pattern. In this study we investigated the effect and clinical relevance of the use of an AFO on walking ability in patients suffering drop foot syndrome. Two Machine Learning (ML) algorithms were implemented on Knime Analytic Platform: Decision Tree (DT) and Random Forest (RF). They were used on a set of spatio-temporal gait variables assessed by means of the IMU-based wearable system for gait analysis, namely Opal Mobility Lab by APDM, on 19 patients with unilateral drop foot syndrome, in two separate conditions: wearing or not the AFO. The following evaluation metrics were used to evaluate the performances of the two tree-based ML models: Recall, Precision, Sensitivity, Specificity, F-measure, Accuracy and Area under the Receiver Operating Characteristic Curve (AucRoc). Moreover a Feature Importance analysis was carried out. Results demonstrate RF was perfect in distinguishing the two classes - walking with and without AFO - while DT achieved a slightly lower classification score but still of high discriminative power, confirming the effective incidence of the use of the AFO on walking pattern. The following parameters were identified as the most informative by the Feature Importance analysis: stance and swing phase duration, cadence, step duration and stride length.

Additive manufacturing offers several advantages when compared to conventional manufacturing technologies, especially where large scale customization is requested. In this article, a procedure for the design of a fully-customized 3D-printed ankle-foot orthosis (AFO) is described, with reference to a case study regarding a 21-year-old woman with an injured ankle. The first step is the acquisition of the geometrical data from the patient’s foot: this is done using photogrammetry. The second step is the data conversion and import in a CAD modeler; SolidWorks™ is chosen for this purpose. The AFO is modelled parametrically around the foot mesh and optimized in order to resist the predicted mechanical stresses. Finally, the device is 3D-printed on an FDM printer and tested on the patient. An excellent geometrical correspondence between the AFO and the patient’s foot is highlighted: this leads to great comfort and enhances medical functionality. The described procedure can be easily automated, further reducing the lead time and costs of the whole process