Abstract
Biometric authentication (i.e., verification of a given subject’s identity using biological characteristics) relying on gait characteristics obtained in a non-intrusive way can be very useful in the area of security, for smart surveillance and access control. In this contribution, we investigated the possibility of carrying out subject identification based on a predictive model built using machine learning techniques, and features extracted from 3-D body joint data provided by a single low-cost RGB-D camera (Microsoft Kinect v2). We obtained a dataset including 400 gait cycles from 20 healthy subjects, and 25 anthropometric measures and gait parameters per gait cycle. Different machine learning algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines, multilayer perceptron, and multilayer perceptron ensemble. The algorithm that led to the model with best trade-off between the considered evaluation metrics was the random forest: overall accuracy of 99%, class accuracy of 100±Â0%, and F 1 score of 99±Â2%. These results show the potential of using a RGB-D camera for subject identification based on quantitative gait analysis. © 2020, Springer Nature Switzerland AG.

Abstract
DBS surgery is considered for Parkinson's Disease patients when motor complications and consequent quality of life is no longer acceptable on optimal medical therapy prescribed by neurologists. Within the operating room, the electrode placement with the best clinical outcome for the patient is quantitatively assessed via the wrist rigidity assessment. A subjective scale is used, influenced by the neurologists' perception and experience. Our research group has previously designed a novel, comfortable and wireless system aiming to tackle this subjectivity. This system comprised a gyroscope sensor in a textile band, placed in the patients' hand, which communicated its measurement to a Smartphone via Bluetooth. During the wrist rigidity evaluation exam, a signal descriptor was computed from angular velocity (omega) and a polynomial mathematical model was used to classify the signals using a quantitative scale of rigidity improvement. In this present work, we aim to develop models that consider the 3-gyroscope-axes to acquire the omega and the cogwheel rigidity. Our results showed that y-gyroscope-axis remains the best way to classify the rigidity reduction, showing an accuracy of 78% and a mean error of 3.5%. According to previous results, the performance was similar and the decrease of samples to extract the omega features did not compromise system performance. The cogwheel rigidity did not improve the previous model and other gyroscope-axis beyond the y-axis decreased system performance.

Abstract
Motion analysis systems based on a single markerless RGB-D camera are more suitable for clinical practice than multi-camera marker-based reference systems. Nevertheless, the validity of RGB-D cameras for motor function assessment in some diseases affecting gait, such as Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP), is yet to be investigated. In this study, the agreement between the Kinect v2 and a reference system for obtaining spatiotemporal and kinematic gait parameters was evaluated in the context of TTR-FAP. 3-D body joint data provided by both systems were acquired from ten TTR-FAP symptomatic patients, while performing ten gait trials. For each gait cycle, we computed several spatiotemporal and kinematic gait parameters. We then determined, for each parameter, the Bland Altman’s bias and 95% limits of agreement, as well as the Pearson’s and concordance correlation coefficients, between systems. The obtained results show that an affordable, portable and non-invasive system based on an RGB-D camera can accurately obtain most of the studied gait parameters (excellent or good agreement for eleven spatiotemporal and one kinematic). This system can bring more objectivity to motor function assessment of polyneuropathy patients, potentially contributing to an improvement of TTR-FAP treatment and understanding, with great benefits to the patients’ quality of life.

Abstract
Objective: Axial motor features are common in Parkinson's disease (PD). These include gait impairment and postural abnormalities, such as camptocormia. The response of these symptoms to deep brain stimulation (DBS) is variable and difficult to assess objectively. For the first time, this study analyzes the treatment outcomes of two PD patients with camptocormia that underwent bilateral subthalamic nucleus (STN)-DBS evaluated with disruptive technologies. Patients and methods: Two patients with PD and camptocormia who underwent STN-DBS were included. Gait parameters were quantitatively assessed before and after surgery by using the NeuroKinect system and the camptocormia angle was measured using the camptoapp. Results: After surgery, patient 1 improved 29 points in the UPDRS-III. His camptocormia angle was 68 degrees before and 38 degrees after surgery. Arm and knee angular amplitudes (117.32 +/- 7.47 vs 134.77 +/- 2.70; 144.51 +/- 7.47 vs 169.08 +/- 3.27) and arm swing (3.59 +/- 2.66 vs 5.40 +/- 1.76 cm) improved when compared with his pre-operative measurements. Patient 2 improved 22 points in the UPDRS-III after surgery. Her camptocormia mostly resolved (47 degrees before to 9 degrees after surgery). Gait analysis revealed improvement of stride length (0.29 +/- 0.03 vs 0.35 +/- 0.03 m), stride width (18.25 +/- 1.16 vs 17.9 +/- 0.84 cm), step velocity (0.91 +/- 0.57 vs 1.33 +/- 0.48 m/s), arm swing (4.51 +/- 1.01 vs 7.38 +/- 2.71 cm) and arm and hip angular amplitudes (131.57 +/- 2.45 degrees vs 137.75 +/- 3.18; 100.51 +/- 1.56 vs 102.18 +/- 1.77 degrees) compared with her preoperative results. Conclusion: The gait parameters and camptocormia of both patients objectively improved after surgery, as assessed by the two quantitative measurement systems. STN-DBS might have a beneficial effect on controlling axial posturing and gait, being a potential surgical treatment for camptocormia in patients with PD. However, further studies are needed to derive adequate selection criteria for this patient population.

Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a rare and disabling neurological disorder caused by, a mutation of the transthyretin gene. One of the disease's characteristics that mostly affects patients' quality of life is its influence on locomotion, with a variable evolution timing. Quantitative motion analysis is useful for assessing motor function, including gait, in diseases affecting movement. However, it is still an evolving field, especially in TTR-FAP, with only a few available studies. A single markerless RGB-D camera pros ides 3-D body joint data in a less expensive, more portable and less intrusive way than reference multi-camera marker-based systems for motion capture. In this contribution, we investigate if a gait analysis system based on a RGB-D camera can be used to detect gait changes over time for a given TTR-FAP patient. 3-D data provided by that system and a reference system were acquired from six TTR-FAP patients, while performing a simple gait task, once and then a year and a half later. For each gait cycle and system, several gait parameters were computed. For each patient, we investigated if the RBG-D camera system is able to detect the existence or not of statistically significant differences between the two different acquisitions (separated by 1.5 years of disease evolution), in a similar way to the reference system. The obtained results show the potential of using a single RGB-D camera to detect relevant changes in spatiotemporal gait parameters (e.g., stride duration and stride length), during TTR-FAP patient follow-up.