Corrigendum: Clinical 3-D gait assessment of patients with polyneuropathy associated with hereditary transthyretin amyloidosis.

[This corrects the article DOI: 10.3389/fneur.2020.605282.].

Gait Characterization and Analysis of Hereditary Amyloidosis Associated with Transthyretin Patients: A Case Series.

Hereditary amyloidosis associated with transthyretin (ATTRv), is a rare autosomal dominant disease characterized by length-dependent symmetric polyneuropathy that has gait impairment as one of its consequences. The gait pattern of V30M ATTRv amyloidosis patients has been described as similar to that of diabetic neuropathy, associated with steppage, but has never been quantitatively characterized. In this study we aim to characterize the gait pattern of patients with V30M ATTRv amyloidosis, thus providing information for a better understanding and potential for supporting diagnosis and disease progression evaluation. We present a case series in which we conducted two gait analyses, 18 months apart, of five V30M ATTRv amyloidosis patients using a 12-camera, marker based, optical system as well as six force platforms. Linear kinematics, ground reaction forces, and angular kinematics results are analyzed for all patients. All patients, except one, showed a delayed toe-off in the second assessment, as well as excessive pelvic rotation, hip extension and external transverse rotation and knee flexion (in stance and swing phases), along with reduced vertical and mediolateral ground reaction forces. The described gait anomalies are not clinically quantified; thus, gait analysis may contribute to the assessment of possible disease progression along with the clinical evaluation.

Supporting the Assessment of Hereditary Transthyretin Amyloidosis Patients Based On 3-D Gait Analysis and Machine Learning.

Hereditary Transthyretin Amyloidosis (vATTR-V30M) is a rare and highly incapacitating sensorimotor neuropathy caused by an inherited mutation (Val30Met), which typically affects gait, among other symptoms. In this context, we investigated the possibility of using machine learning (ML) techniques to build a model(s) that can be used to support the detection of the Val30Met mutation (possibility of developing the disease), as well as symptom onset detection for the disease, given the gait characteristics of a person. These characteristics correspond to 24 gait parameters computed from 3-D body data, provided by a Kinect v2 camera, acquired from a person while walking towards the camera. To build the model(s), different ML algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines (SVM), and multilayer perceptron. For a dataset corresponding to 66 subjects (25 healthy controls, 14 asymptomatic mutation carriers, and 27 patients) and several gait cycles per subject, we were able to obtain a model that distinguishes between controls and vATTR-V30M mutation carriers (with or without symptoms) with a mean accuracy of 92% (SVM). We also obtained a model that distinguishes between asymptomatic and symptomatic carriers with a mean accuracy of 98% (SVM). These results are very relevant, since this is the first study that proposes a ML approach to support vATTR-V30M patient assessment based on gait, being a promising foundation for the development of a computer-aided diagnosis tool to help clinicians in the identification and follow-up of this disease. Furthermore, the proposed method may also be used for other neuropathies.

Clinical 3-D Gait Assessment of Patients With Polyneuropathy Associated With Hereditary Transthyretin Amyloidosis.

Hereditary amyloidosis associated with transthyretin V30M (ATTRv V30M) is a rare and inherited multisystemic disease, with a variable presentation and a challenging diagnosis, follow-up and treatment. This condition entails a definitive and progressive motor impairment that compromises walking ability from near onset. The detection of the latter is key for the disease's diagnosis. The aim of this work is to perform quantitative 3-D gait analysis in ATTRv V30M patients, at different disease stages, and explore the potential of the obtained gait information for supporting early diagnosis and/or stage distinction during follow-up. Sixty-six subjects (25 healthy controls, 14 asymptomatic ATTRv V30M carriers, and 27 symptomatic patients) were included in this case-control study. All subjects were asked to walk back and forth for 2 min, in front of a Kinect v2 camera prepared for body motion tracking. We then used our own software to extract gait-related parameters from the camera's 3-D body data. For each parameter, the main subject groups and symptomatic patient subgroups were statistically compared. Most of the explored gait parameters can potentially be used to distinguish between the considered group pairs. Despite of statistically significant differences being found, most of them were undetected to the naked eye. Our Kinect camera-based system is easy to use in clinical settings and provides quantitative gait information that can be useful for supporting clinical assessment during ATTRv V30M onset detection and follow-up, as well as developing more objective and fine-grained rating scales to further support the clinical decisions.

SnapKi-An Inertial Easy-to-Adapt Wearable Textile Device for Movement Quantification of Neurological Patients.

The development of wearable health systems has been the focus of many researchers who aim to find solutions in healthcare. Additionally, the large potential of textiles to integrate electronics, together with the comfort and usability they provide, has contributed to the development of smart garments in this area. In the field of neurological disorders with motor impairment, clinicians look for wearable devices that may provide quantification of movement symptoms. Neurological disorders affect different motion abilities thus requiring different needs in movement quantification. With this background we designed and developed an inertial textile-embedded wearable device that is adaptable to different movement-disorders quantification requirements. This adaptative device is composed of a low-power 9-axis inertial unit, a customised textile band and a web and Android cross application used for data collection, debug and calibration. The textile band comprises a snap buttons system that allows the attachment of the inertial unit, as well as its connection with the analog sensors through conductive textile. The resulting system is easily adaptable for quantification of multiple motor symptoms in different parts of the body, such as rigidity, tremor and bradykinesia assessments, gait analysis, among others. In our project, the system was applied for a specific use-case of wrist rigidity quantification during Deep Brain Stimulation surgeries, showing its high versatility and receiving very positive feedback from patients and doctors.

iHandU: A Novel Quantitative Wrist Rigidity Evaluation Device for Deep Brain Stimulation Surgery.

Deep brain stimulation (DBS) surgery is the gold standard therapeutic intervention in Parkinson's disease (PD) with motor complications, notwithstanding drug therapy. In the intraoperative evaluation of DBS's efficacy, neurologists impose a passive wrist flexion movement and qualitatively describe the perceived decrease in rigidity under different stimulation parameters and electrode positions. To tackle this subjectivity, we designed a wearable device to quantitatively evaluate the wrist rigidity changes during the neurosurgery procedure, supporting physicians in decision-making when setting the stimulation parameters and reducing surgery time. This system comprises a gyroscope sensor embedded in a textile band for patient's hand, communicating to a smartphone via Bluetooth and has been evaluated on three datasets, showing an average accuracy of 80%. In this work, we present a system that has seen four iterations since 2015, improving on accuracy, usability and reliability. We aim to review the work done so far, outlining the iHandU system evolution, as well as the main challenges, lessons learned, and future steps to improve it. We also introduce the last version (iHandU 4.0), currently used in DBS surgeries at São João Hospital in Portugal.

Validation of a Single RGB-D Camera for Gait Assessment of Polyneuropathy Patients.

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.

Automated and objective measures of gait dynamics in camptocormia Parkinson's Disease subthalamic deep brain stimulation.

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° before and 38° after surgery. Arm and knee angular amplitudes (117.32 ±â€¯7.47 vs 134.77 ±â€¯2.70°; 144.51 ±â€¯7.47 vs 169.08 ±â€¯3.27°) and arm swing (3.59 ±â€¯2.66 vs 5.40 ±â€¯1.76 cm) improved when compared with his preoperative measurements. Patient 2 improved 22 points in the UPDRS-III after surgery. Her camptocormia mostly resolved (47° before to 9° after surgery). Gait analysis revealed improvement of stride length (0.29 ±â€¯0.03 vs 0.35 ±â€¯0.03 m), stride width (18.25 ±â€¯1.16 vs 17.9 ±â€¯0.84 cm), step velocity (0.91 ±â€¯0.57 vs 1.33 ±â€¯0.48 m/s), arm swing (4.51 ±â€¯1.01 vs 7.38 ±â€¯2.71 cm) and arm and hip angular amplitudes (131.57 ±â€¯2.45° vs 137.75 ±â€¯3.18; 100.51 ±â€¯1.56 vs 102.18 ±â€¯1.77°) 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.

Full-body motion assessment: Concurrent validation of two body tracking depth sensors versus a gold standard system during gait.

RGB-D cameras provide 3-D body joint data in a low-cost, portable and non-intrusive way, when compared with reference motion capture systems used in laboratory settings. In this contribution, we evaluate the validity of both Microsoft Kinect versions (v1 and v2) for motion analysis against a Qualisys system in a simultaneous protocol. Two different walking directions in relation to the Kinect (towards - WT, and away - WA) were explored. For each gait trial, measures related with all body parts were computed: velocity of all joints, distance between symmetrical joints, and angle at some joints. For each measure, we compared each Kinect version and Qualisys by obtaining the mean true error and mean absolute error, Pearson's correlation coefficient, and optical-to-depth ratio. Although both Kinect v1 and v2 and/or WT and WA data present similar accuracy for some measures, better results were achieved, overall, when using WT data provided by the Kinect v2, especially for velocity measures. Moreover, the velocity and distance presented better results than angle measures. Our results show that both Kinect versions can be an alternative to more expensive systems such as Qualisys, for obtaining distance and velocity measures as well as some angles metrics (namely the knee angles). This conclusion is important towards the off-lab non-intrusive assessment of motor function in different areas, including sports and healthcare.

System for automatic gait analysis based on a single RGB-D camera.

Human gait analysis provides valuable information regarding the way of walking of a given subject. Low-cost RGB-D cameras, such as the Microsoft Kinect, are able to estimate the 3-D position of several body joints without requiring the use of markers. This 3-D information can be used to perform objective gait analysis in an affordable, portable, and non-intrusive way. In this contribution, we present a system for fully automatic gait analysis using a single RGB-D camera, namely the second version of the Kinect. Our system does not require any manual intervention (except for starting/stopping the data acquisition), since it firstly recognizes whether the subject is walking or not, and identifies the different gait cycles only when walking is detected. For each gait cycle, it then computes several gait parameters, which can provide useful information in various contexts, such as sports, healthcare, and biometric identification. The activity recognition is performed by a predictive model that distinguishes between three activities (walking, standing and marching), and between two postures of the subject (facing the sensor, and facing away from it). The model was built using a multilayer perceptron algorithm and several measures extracted from 3-D joint data, achieving an overall accuracy and F1 score of 98%. For gait cycle detection, we implemented an algorithm that estimates the instants corresponding to left and right heel strikes, relying on the distance between ankles, and the velocity of left and right ankles. The algorithm achieved errors for heel strike instant and stride duration estimation of 15 ± 25 ms and 1 ± 29 ms (walking towards the sensor), and 12 ± 23 ms and 2 ± 24 ms (walking away from the sensor). Our gait cycle detection solution can be used with any other RGB-D camera that provides the 3-D position of the main body joints.

Movement Quantification in Neurological Diseases: Methods and Applications.

The movement of the human body offers neurologists important clues for the diagnosis and follow-up of many neurological diseases. The typical diagnosis approach is accomplished through simple observation of movements of interest (MOI) associated with a specific neurological disease. This approach is highly subjective because it is mainly based on qualitative evaluation of MOIs. Quantitative movement techniques are then obvious diagnosis-aid systems to approach these cases. Nevertheless, the use of motion quantification techniques in these pathologies is still relatively rare. In this paper, we intend to review this area and provide a clear picture of the current state of the art, both in the methods used and their applications to the main movement-related neurological diseases. We approach some historic aspects and the current state of the motion capture techniques and present the results of a survey to the literature that includes 82 papers, since 2006, covering the usage of these techniques in neurological diseases. Furthermore, we discuss the pros and cons of using quantitative approaches in these clinical scenarios. Finally, we present some conclusions and discuss the trends we foresee for the future.