Computer Vision for Gait Assessment in Cerebral Palsy: Metric Learning and Confidence Estimation.

Assessing the motor impairments of individuals with neurological disorders holds significant importance in clinical practice. Currently, these clinical assessments are time-intensive and depend on qualitative scales administered by trained healthcare professionals at the clinic. These evaluations provide only coarse snapshots of a person's abilities, failing to track quantitatively the detail and minutiae of recovery over time. To overcome these limitations, we introduce a novel machine learning approach that can be administered anywhere including home. It leverages a spatial-temporal graph convolutional network (STGCN) to extract motion characteristics from pose data obtained from monocular video captured by portable devices like smartphones and tablets. We propose an end-to-end model, achieving an accuracy rate of approximately 76.6% in assessing children with Cerebral Palsy (CP) using the Gross Motor Function Classification System (GMFCS). This represents a 5% improvement in accuracy compared to the current state-of-the-art techniques and demonstrates strong agreement with professional assessments, as indicated by the weighted Cohen's Kappa ( κlw = 0.733 ). In addition, we introduce the use of metric learning through triplet loss and self-supervised training to better handle situations with a limited number of training samples and enable confidence estimation. Setting a confidence threshold at 0.95 , we attain an impressive estimation accuracy of 88% . Notably, our method can be efficiently implemented on a wide range of mobile devices, providing real-time or near real-time results.

Freezing of gait assessment with inertial measurement units and deep learning: effect of tasks, medication states, and stops.

BACKGROUND: Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson's Disease (PD). Traditionally, FOG assessment relies on time-consuming visual inspection of camera footage. Therefore, previous studies have proposed portable and automated solutions to annotate FOG. However, automated FOG assessment is challenging due to gait variability caused by medication effects and varying FOG-provoking tasks. Moreover, whether automated approaches can differentiate FOG from typical everyday movements, such as volitional stops, remains to be determined. To address these questions, we evaluated an automated FOG assessment model with deep learning (DL) based on inertial measurement units (IMUs). We assessed its performance trained on all standardized FOG-provoking tasks and medication states, as well as on specific tasks and medication states. Furthermore, we examined the effect of adding stopping periods on FOG detection performance. METHODS: Twelve PD patients with self-reported FOG (mean age 69.33 ± 6.02 years) completed a FOG-provoking protocol, including timed-up-and-go and 360-degree turning-in-place tasks in On/Off dopaminergic medication states with/without volitional stopping. IMUs were attached to the pelvis and both sides of the tibia and talus. A temporal convolutional network (TCN) was used to detect FOG episodes. FOG severity was quantified by the percentage of time frozen (%TF) and the number of freezing episodes (#FOG). The agreement between the model-generated outcomes and the gold standard experts' video annotation was assessed by the intra-class correlation coefficient (ICC). RESULTS: For FOG assessment in trials without stopping, the agreement of our model was strong (ICC (%TF) = 0.92 [0.68, 0.98]; ICC(#FOG) = 0.95 [0.72, 0.99]). Models trained on a specific FOG-provoking task could not generalize to unseen tasks, while models trained on a specific medication state could generalize to unseen states. For assessment in trials with stopping, the agreement of our model was moderately strong (ICC (%TF) = 0.95 [0.73, 0.99]; ICC (#FOG) = 0.79 [0.46, 0.94]), but only when stopping was included in the training data. CONCLUSION: A TCN trained on IMU signals allows valid FOG assessment in trials with/without stops containing different medication states and FOG-provoking tasks. These results are encouraging and enable future work investigating automated FOG assessment during everyday life.

Patterns and assessment of spastic hemiplegic gait.

Hemiparetic gait disorders are common in stroke survivors. A circumductory gait is often considered the typical hemiparetic gait. In clinical practice, a wide spectrum of abnormal gait patterns is observed, depending on the severity of weakness and spasticity, and the anatomical distribution of spasticity. Muscle strength is the key determinant of gait disorders in hemiparetic stroke survivors. Spasticity and its associated involuntary activation of synergistic spastic muscles often alter posture of involved joint(s) and subsequently the alignment of hip, knee, and ankle joints, resulting in abnormal gait patterns. Due to combinations of various levels of muscle weakness and spasticity and their interactions with ground reaction force, presentations of gait disorders are variable. From a neuromechanical perspective, a stepwise visual gait analysis approach is proposed to identify primary underlying causes. In this approach, the pelvic and hip joint movement is examined first. The pelvic girdle constitutes three kinematic determinants. Its abnormality determines the body vector and compensatory kinetic chain reactions in the knee and ankle joints. The second step is to assess the ankle and foot complex abnormality. The last step is to examine abnormality of the knee joint. Assessment of muscle strength and spasticity of hip, knee, and ankle/foot joints needs to be performed before these steps. Lidocaine nerve blocks can be a useful diagnostic tool. Recognizing different patterns and identifying the primary causes are critical to developing clinical interventions to improve gait functions.

Neurological gait assessment.

Gait disorders are a common feature of neurological disease. The gait examination is an essential part of the neurological clinical assessment, providing valuable clues to a myriad of causes. Understanding how to examine gait is not only essential for neurological diagnosis but also for treatment and prognosis. Here, we review aspects of the clinical history and examination of neurological gait to help guide gait disorder assessment. We focus particularly on how to differentiate between common gait abnormalities and highlight the characteristic features of the more prevalent neurological gait patterns such as ataxia, waddling, steppage, spastic gait, Parkinson's disease and functional gait disorders. We also offer diagnostic clues for some unusual gait presentations, such as dystonic, stiff-person and choreiform gait, along with red flags that help differentiate atypical parkinsonism from Parkinson's disease.

Assessment of the psychometric properties of the Italian version of the New Freezing of Gait Questionnaire (NFOG-Q-IT) in people with Parkinson disease: a validity and reliability study.

INTRODUCTION: Freezing of gait (FOG) in Parkinson's disease (PD) is a challenging clinical symptom to assess, due to its episodic nature. A valid and reliable tool is the New FOG Questionnaire (NFOG-Q) used worldwide to measure FOG symptoms in PD. OBJECTIVE: The aim of this study was to translate, to culturally adapt, and to test the psychometric characteristics of the Italian version of the NFOG-Q (NFOG-Q-It). METHODS: The translation and cultural adaptation was based on ISPOR TCA guidelines to finalize the 9-item NFOG-Q-It. Internal consistency was assessed in 181 Italian PD native speakers who experienced FOG using Cronbach's alpha. Cross-cultural analysis was tested using the Spearman's correlation between the NFOG-Q-It and the Modified Hoehn-Yahr Scale (M-H&Y). To assess construct validity, correlations among NFOG-Q-It, Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), the Falls Efficacy Scale-International (FES-I), the 6-min Walking Test (6MWT), the Mini Balance Evaluation System Test (Mini-BESTest) and the Short Physical Performance Battery (SPPB) were investigated. RESULTS: The Italian N-FOGQ had high internal consistency (Cronbach's α = 0.859). Validity analysis showed significant correlations between NFOG-Q-IT total score and M-H&Y scores (r = 0.281 p < 0.001), MDS-UPDRS (r = 0.359 p < 0.001), FES-I (r = 0.230 p = 0.002), Mini BESTest (r = -0.256 p = 0.001) and 6MWT (r = -0.166 p = 0.026). No significant correlations were found with SPPB, MOCA and MMSE. CONCLUSION: The NFOG-It is a valuable and reliable tool for assessing FOG symptoms, duration and frequency in PD subjects. Results provide the validity of NFOG-Q-It by reproducing and enlarging previous psychometric data.

Developing the Freezing of Gait Severity Tool: A Delphi consensus study to determine the content of a clinician-rated assessment for freezing of gait severity.

OBJECTIVES: There is no standardisation of tasks or measures for evaluation of freezing of gait severity in people with Parkinson's disease. This study aimed to develop a clinician-rated tool for freezing of gait severity (i.e. Freezing of Gait Severity Tool), through determining clinicians' ratings of the most important triggering circumstances to be examined and aspects of freezing of gait to be measured. DESIGN: A three-round, web-based Delphi study. PARTICIPANTS: Healthcare professionals, with at least five years' experience in managing freezing of gait in people with Parkinson. MAIN OUTCOME MEASURES: Round 1 required participants (n = 28) to rate items on a 5-point Likert scale, based on priority for inclusion in the Freezing of Gait Severity Tool. In Round 2, participants (n = 18) ranked the items based on priority for inclusion. In Round 3, participants (n = 18) confirmed or rejected the shortlisted items by judging their ability, on a binary scale, to screen for freezing of gait, detect changes in freezing severity, and discriminate between degrees of severity. RESULTS: Participants agreed with the triggering circumstances of turning hesitation, narrow space hesitation, start hesitation, cognitive dual-tasking, and open space hesitation should be assessed; and the aspects of gait freezing to be measured included freezing type, number of freezing episodes during a task, and average duration of freezing episodes. CONCLUSIONS: This study attained a consensus for the items to be included in a clinician-rated tool for freezing of gait severity. Future studies should investigate psychometric properties and clinical feasibility of the Freezing of Gait Severity Tool.

Quantitative assessment of gait characteristics in patients with Parkinson's disease using 2D video.

INTRODUCTION: Gait disorders are common in patients with Parkinson's disease (PD), and objective, quantifiable, and portable evaluation methods are needed in clinical practice. We assessed the differences in gait characteristics between patients with PD and healthy controls (HCs) using two-dimensional (2D) video and explored the diagnostic value of 2D video for early-stage PD. METHODS: Sixty-eight patients with PD and 48 HCs were recruited. All participants walked a standard 5-m track while recorded using mobile phones. The differences in gait characteristics between the groups and gait parameters of patients with early-stage PD were analyzed. Receiver operating characteristic analysis was used to evaluate the discrimination activity of gait parameters. RESULTS: Compared with HCs, patients with PD had shorter step length (S/M, p < 0.001/0.001), faster cadence (p = 0.044), slower walking speed (p < 0.001), smaller arm-swing angle (S/M, p < 0.001/0.001), slower arm-swing velocity (S/M, p < 0.001/0.001), and greater variability and asymmetry. The patients with early-stage PD had similar characteristics. After combined step length and variability and asymmetry of arm-swing angle, diagnostic sensitivity and specificity reached 84.6% and 89.6%, respectively, and an area under the curve of 0.91 (0.84-0.97). CONCLUSION: We proposed a new method to improve the sensitivity of early-stage PD diagnosis using 2D video, which, as a portable and objective method, could be used to evaluate gait disorders in PD, including in early-stage PD. Combined diagnostic analysis of step length and variability and asymmetry of arm-swing angle could improve the sensitivity of PD diagnosis and was helpful in distinguishing patients with early-stage PD from HCs.

Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks.

BACKGROUND: Freezing of gait (FOG) is a common and debilitating gait impairment in Parkinson's disease. Further insight into this phenomenon is hampered by the difficulty to objectively assess FOG. To meet this clinical need, this paper proposes an automated motion-capture-based FOG assessment method driven by a novel deep neural network. METHODS: Automated FOG assessment can be formulated as an action segmentation problem, where temporal models are tasked to recognize and temporally localize the FOG segments in untrimmed motion capture trials. This paper takes a closer look at the performance of state-of-the-art action segmentation models when tasked to automatically assess FOG. Furthermore, a novel deep neural network architecture is proposed that aims to better capture the spatial and temporal dependencies than the state-of-the-art baselines. The proposed network, termed multi-stage spatial-temporal graph convolutional network (MS-GCN), combines the spatial-temporal graph convolutional network (ST-GCN) and the multi-stage temporal convolutional network (MS-TCN). The ST-GCN captures the hierarchical spatial-temporal motion among the joints inherent to motion capture, while the multi-stage component reduces over-segmentation errors by refining the predictions over multiple stages. The proposed model was validated on a dataset of fourteen freezers, fourteen non-freezers, and fourteen healthy control subjects. RESULTS: The experiments indicate that the proposed model outperforms four state-of-the-art baselines. Moreover, FOG outcomes derived from MS-GCN predictions had an excellent (r = 0.93 [0.87, 0.97]) and moderately strong (r = 0.75 [0.55, 0.87]) linear relationship with FOG outcomes derived from manual annotations. CONCLUSIONS: The proposed MS-GCN may provide an automated and objective alternative to labor-intensive clinician-based FOG assessment. Future work is now possible that aims to assess the generalization of MS-GCN to a larger and more varied verification cohort.

Application of nonlinear analysis for the assessment of gait in patients with Parkinson's disease.

BACKGROUND: Gait can be affected by diseases such as Parkinson's disease (PD), which lead to alterations like shuffle gait or loss of balance. PD diagnosis is based on subjective measures to generate a score using the Unified Parkinson's Disease Rating Scale (UPDRS). To improve clinical assessment accuracy, gait analysis can utilise linear and nonlinear methods. A nonlinear method called the Lyapunov exponent (LE) is being used to identify chaos in dynamic systems. This article presents an application of LE for diagnosing PD. OBJECTIVE: The objectives were to use the largest Lyapunov exponents (LaLyEx), sample entropy (SampEn) and root mean square (RMS) to assess the gait of subjects diagnosed with PD; to verify the applicability of these parameters to distinguish between people with PD and healthy controls (CO); and to differentiate subjects within the PD group according to the UPDRS assessment. METHODS: The subjects were divided into the CO group (n= 12) and the PD group (n= 14). The PD group was also divided according to the UPDRS score: UPDRS 0 (n= 7) and UPDRS 1 (n= 7). Kinematic data of lower limbs were measured using inertial measurement units (IMU) and nonlinear parameters (LaLyEx, SampEn and RMS) were calculated. RESULTS: There were significant differences between the CO and PD groups for RMS, SampEn and the LaLyEx. After dividing the PD group according to the UPDRS score, there were significant differences in LaLyEx and RMS. CONCLUSIONS: The selected parameters can be used to distinguish people with PD from CO subjects, and separate people with PD according to the UPDRS score.

Validation of the Gait Outcomes Assessment List questionnaire and caregiver priorities for individuals with cerebral palsy.

AIM: To expand upon previous validation of the Gait Outcomes Assessment List (GOAL) questionnaire in individuals with cerebral palsy (CP), to rank items by importance, and to summarize written-in (free text) goals. METHOD: For this cross-sectional study, the parent-version 5.0 of the GOAL was completed by 310 consecutive caregivers of 310 individuals aged 3 to 25 years with CP (189 males, 121 females; mean [SD] age: 10y [4y 2mo]; Gross Motor Function Classification System [GMFCS] levels I-IV) concurrent with a gait analysis. Distribution properties and validity were quantified using questionnaires, kinematics, and oxygen consumption. Items classified as at least 'difficult' to perform and 'very important' to improve were considered caregiver priorities and rank ordered. Free text goals were categorized. Results were summarized for everyone and by GMFCS level. RESULTS: Most scores were normally distributed. Validity was acceptable, with concurrent greater than construct validity. Among all 310 caregivers, fatigue was the highest priority, followed by gait pattern and appearance items. The rank of priorities varied by GMFCS level. Common free text goals included toileting independently as well as improved fine motor and ball sport skills. INTERPRETATION: The GOAL is a valid tool that can help prioritize goals across GMFCS levels I to IV. Identifying the top goals may improve shared decision-making and prioritize research for this sample.

A-GAS: A Probabilistic Approach for Generating Automated Gait Assessment Score for Cerebral Palsy Children.

Gait disorders in children with cerebral palsy (CP) affect their mental, physical, economic, and social lives. Gait assessment is one of the essential steps of gait management. It has been widely used for clinical decision making and evaluation of different treatment outcomes. However, most of the present methods of gait assessment are subjective, less sensitive to small pathological changes, time-taking and need a great effort of an expert. This work proposes an automated, comprehensive gait assessment score (A-GAS) for gait disorders in CP. Kinematic data of 356 CP and 41 typically developing subjects is used to validate the performance of A-GAS. For the computation of A-GAS, instance abnormality index (AII) and abnormality index (AI) are calculated. AII quantifies gait abnormality of a gait cycle instance, while AI quantifies gait abnormality of a joint angle profile during walking. AII is calculated for all gait cycle instances by performing probabilistic and statistical analyses. Abnormality index (AI) is a weighted sum of AII, computed for each joint angle profile. A-GAS is a weighted sum of AI, calculated for a lower limb. Moreover, a graphical representation of the gait assessment report, including AII, AI, and A-GAS is generated for providing a better depiction of the assessment score. Furthermore, the work compares A-GAS with a present rating-based gait assessment scores to understand fundamental differences. Finally, A-GAS's performance is verified for a high-cost multi-camera set-up using nine joint angle profiles and a low-cost single camera set-up using three joint angle profiles. Results show no significant differences in performance of A-GAS for both the set-ups. Therefore, A-GAS for both the set-ups can be used interchangeably.

Guidelines for Management of Idiopathic Normal Pressure Hydrocephalus (Third Edition): Endorsed by the Japanese Society of Normal Pressure Hydrocephalus.

Among the various disorders that manifest with gait disturbance, cognitive impairment, and urinary incontinence in the elderly population, idiopathic normal pressure hydrocephalus (iNPH) is becoming of great importance. The first edition of these guidelines for management of iNPH was published in 2004, and the second edition in 2012, to provide a series of timely, evidence-based recommendations related to iNPH. Since the last edition, clinical awareness of iNPH has risen dramatically, and clinical and basic research efforts on iNPH have increased significantly. This third edition of the guidelines was made to share these ideas with the international community and to promote international research on iNPH. The revision of the guidelines was undertaken by a multidisciplinary expert working group of the Japanese Society of Normal Pressure Hydrocephalus in conjunction with the Japanese Ministry of Health, Labour and Welfare research project. This revision proposes a new classification for NPH. The category of iNPH is clearly distinguished from NPH with congenital/developmental and acquired etiologies. Additionally, the essential role of disproportionately enlarged subarachnoid-space hydrocephalus (DESH) in the imaging diagnosis and decision for further management of iNPH is discussed in this edition. We created an algorithm for diagnosis and decision for shunt management. Diagnosis by biomarkers that distinguish prognosis has been also initiated. Therefore, diagnosis and treatment of iNPH have entered a new phase. We hope that this third edition of the guidelines will help patients, their families, and healthcare professionals involved in treating iNPH.

Validity and sensitivity of instrumented postural and gait assessment using low-cost devices in Parkinson's disease.

BACKGROUND: Accurate assessment of balance and gait is necessary to monitor the clinical progress of Parkinson's disease (PD). Conventional clinical scales can be biased and have limited accuracy. Novel interactive devices are potentially useful to detect subtle posture or gait-related impairments. METHODS: Posturographic and single and dual-task gait assessments were performed to 54 individuals with PD and 43 healthy controls with the Wii Balance Board and the Kinect v2 and the, respectively. Individuals with PD were also assessed with the Tinetti Performance Oriented Mobility Assessment, the Functional Gait Assessment and the 10-m Walking Test. The influence of demographic and clinical variables on the performance in the instrumented posturographic and gait tests, the sensitivity of these tests to the clinical condition and phenotypes, and their convergent validity with clinical scales were investigated. RESULTS: Individuals with PD in H&Y I and I.5 stages showed similar performance to controls. The greatest differences in posture and gait were found between subjects in H&Y II.5 and H&Y I-I.5 stage, as well as controls. Dual-tasking enhanced the differences among all groups in gait parameters. Akinetic/rigid phenotype showed worse postural control and gait than other phenotypes. High significant correlations were found between the limits of stability and most of gait parameters with the clinical scales. CONCLUSIONS: Low-cost devices showed potential to objectively quantify posture and gait in established PD (H&Y ≥ II). Dual-tasking gait evaluation was more sensitive to detect differences among PD stages and compared to controls than free gait. Gait and posture were more impaired in akinetic/rigid PD.

Automatic Diagnosis of Cerebral Palsy Gait Using Computational Intelligence Techniques: A Low-Cost Multi-Sensor Approach.

Automatic diagnosing of Cerebral Palsy (CP) gait is crucial in quantitative evaluation of a therapeutic intervention. Existing systems for such gait assessment are expensive and require user intervention. This study proposes a low-cost gait assessment system equipped with multiple Kinect sensors. Forty subjects (20 CP patients and 20 normal) were recruited for the experiment. To remove outlier frames from the combined gait signal of multiple sensors a data driven algorithm was proposed. Different supervised classifiers along with extreme learning machine were investigated to diagnose CP gait. In addition, a feature level analysis was also performed. Several spatio-temporal features (i.e. step length, stride length, stride time, etc.) were extracted. The strength of walking ratio, a speed invariant feature, to detect CP gait was thoroughly analyzed. The proposed system outperformed state-of-the-art with ≈98% of accuracy (sensitivity: 100%, and specificity: 96.87%). Results indicate a substantial improvement in abnormality detection performance after outlier removal. Based on ReliefF feature ranking algorithm, walking ratio ranked the best among other classical gait features. Performance of all classifiers increased substantially using walking ratio as a feature. Extreme learning machine demonstrated a competing performance in all cases. The higher classification accuracy of this low-cost system using only a single feature makes it attractive for CP gait detection.

Does Curved Walking Sharpen the Assessment of Gait Disorders? An Instrumented Approach Based on Wearable Inertial Sensors.

Gait and balance assessment in the clinical context mainly focuses on straight walking. Despite that curved trajectories and turning are commonly faced in our everyday life and represent a challenge for people with gait disorders. The adoption of curvilinear trajectories in the rehabilitation practice could have important implications for the definition of protocols tailored on individual's needs. The aim of this study was to contribute toward the quantitative characterization of straight versus curved walking using an ecological approach and focusing on healthy and neurological populations. Twenty healthy adults (control group (CG)) and 20 patients with Traumatic Brain Injury (TBI) (9 severe, sTBI-S, and 11 very severe, sTBI-VS) performed a 10 m and a Figure-of-8 Walk Test while wearing four inertial sensors that were located on both tibiae, sternum and pelvis. Spatiotemporal and gait quality indices that were related to locomotion stability, symmetry, and smoothness were obtained. The results show that spatiotemporal, stability, and symmetry-related gait patterns are challenged by curved walking both in healthy subjects and sTBI-S, whereas no difference was displayed for sTBI-VS. The use of straight walking alone to assess gait disorders is thus discouraged, particularly in patients with good walking abilities, in favor of the adoption of complementary tests that were also based on curved paths.

Assessment of Parkinsonian gait in older adults with dementia via human pose tracking in video data.

BACKGROUND: Parkinsonism is common in people with dementia, and is associated with neurodegenerative and vascular changes in the brain, or with exposure to antipsychotic or other dopamine antagonist medications. The detection of parkinsonian changes to gait may provide an opportunity to intervene and address reversible causes. In this study, we investigate the use of a vision-based system as an unobtrusive means to assess severity of parkinsonism in gait. METHODS: Videos of walking bouts of natural gait were collected in a specialized dementia unit using a Microsoft Kinect sensor and onboard color camera, and were processed to extract sixteen 3D and eight 2D gait features. Univariate regression to gait quality, as rated on the Unified Parkinson's Disease Rating Scale (UPDRS) and Simpson-Angus Scale (SAS), was used to identify gait features significantly correlated to these clinical scores for inclusion in multivariate models. Multivariate ordinal logistic regression was subsequently performed and the relative contribution of each gait feature for regression to UPDRS-gait and SAS-gait scores was assessed. RESULTS: Four hundred one walking bouts from 14 older adults with dementia were included in the analysis. Multivariate ordinal logistic regression models incorporating selected 2D or 3D gait features attained similar accuracies: the UPDRS-gait regression models achieved accuracies of 61.4 and 62.1% for 2D and 3D features, respectively. Similarly, the SAS-gait models achieved accuracies of 47.4 and 48.5% with 2D or 3D gait features, respectively. CONCLUSIONS: Gait features extracted from both 2D and 3D videos are correlated to UPDRS-gait and SAS-gait scores of parkinsonism severity in gait. Vision-based systems have the potential to be used as tools for longitudinal monitoring of parkinsonism in residential settings.

Prevalence of Alternative Diagnoses and Implications for Management in Idiopathic Normal Pressure Hydrocephalus Patients.

BACKGROUND: Following Bayes theorem, ventriculomegaly and ataxia confer only a 30% chance of idiopathic Normal Pressure Hydrocephalus (NPH). When coupled with positive responses to best diagnostic testing (extended lumbar drainage), 70% of patients recommended for shunting will not actually have NPH. This is inadequate clinical care. OBJECTIVE: To determine the proportion of alternative and treatable diagnoses in patients referred to a multidisciplinary NPH clinic. METHODS: Patients without previously diagnosed NPH were queried from prospectively collected data. At least 1 neurosurgeon, cognitive neurologist, and neuropsychologist jointly formulated best treatment plans. RESULTS: Of 328 total patients, 45% had an alternative diagnosis; 11% of all patients improved with treatment of an alternative diagnosis. Of 87 patients with treatable conditions, the highest frequency of pathologies included sleep disorders, and cervical stenosis, followed by Parkinson disease. Anti-cholinergic burden was a contributor for multiple patients. Of 142 patients undergoing lumbar puncture, 71% had positive responses and referred to surgery. Compared to NPH patients, mimickers were statistically significantly older with lower Montreal Cognitive Assessment (MoCA) score and worse gait parameters. Overall, 26% of the original patients underwent shunting. Pre-post testing revealed a statistically significant improved MoCA score and gait parameters in those patients who underwent surgery with follow-up. CONCLUSION: Because the Multidisciplinary NPH Clinic selected only 26% for surgery (corroborating 30% in Bayes theorem), an overwhelming majority of patients with suspected NPH will harbor alternative diagnoses. Identification of contributing/confounding conditions will support the meticulous work-up necessary to appropriately manage patients without NPH while optimizing clinical responses to shunting in correctly diagnosed patients.

The effects of cerebrospinal fluid tap-test on idiopathic normal pressure hydrocephalus: an inertial sensors based assessment.

BACKGROUND: Gait disturbances are typical of persons with idiopathic normal pressure hydrocephalus (iNPH) without signs distinctive from other neurodegenerative and vascular conditions. Cerebrospinal fluid tap-test (CSF-TT) is expected to improve the motor performance of iNPH patients and is a prognostic indicator in their surgical management. This observational prospective study aims to determine which spatio-temporal gait parameter(s), measured during instrumented motor tests, and clinical scale(s) may provide a relevant contribution in the evaluation of motor performance pre vs. post CSF-TT on iNPH patients with and without important vascular encephalopathy. METHODS: Seventy-six patients (20 with an associated vascular encephalopathy) were assessed before, and 24 and 72 h after the CSF-TT by a timed up and go test (TUG) and an 18 m walking test (18 mW) instrumented using inertial sensors. Tinetti Gait, Tinetti Balance, Gait Status Scale, and Grading Scale were fulfilled before and 72 h after the CSF-TT. Stride length, cadence and total time were selected as the outcome measures. Statistical models with mixed effects were implemented to determine the relevant contribution to response variables of each quantitative gait parameter and clinical scales. RESULTS AND CONCLUSION: From baseline to 72 h post CSF-TT patients improved significantly by increasing cadence in 18 mW and TUG (on average of 1.7 and 2.4 strides/min respectively) and stride length in 18 mW (on average of 3.1 cm). A significant reduction of gait apraxia was reflected by modifications in double support duration and in coordination index. Tinetti Gait, Tinetti Balance and Gait Status Scale were able to explain part of the variability of response variables not covered by instrumental data, especially in TUG. Grading Scale revealed the highest affinity with TUG total time and cadence when considering clinical scales alone. Patients with iNPH and an associated vascular encephalopathy showed worst performances compared to pure iNPH but without statistical significance. Gait improvement following CSF-TT was comparable in the two groups. Overall these results suggest that, in order to augment CSF-TT accuracy, is key to assess the gait pattern by analyzing the main spatio-temporal parameters and set post evaluation at 72 h. TRIAL REGISTRATION: Approved by ethics committee: CE 14131 23/02/2015.

Random forest-based classsification and analysis of hemiplegia gait using low-cost depth cameras.

Hemiplegia is a form of paralysis that typically has the symptom of dysbasia. In current clinical rehabilitations, to measure the level of hemiplegia gaits, clinicians often conduct subject evaluations through observations, which is unreliable and inaccurate. The Microsoft Kinect sensor (MS Kinect) is a widely used, low-cost depth sensor that can be used to detect human behaviors in real time. The purpose of this study is to investigate the usage of the Kinect data for the classification and analysis of hemiplegia gait. We first acquire the gait data by using a MS Kinect and extract a set of gait features including the stride length, gait speed, left/right moving distances, and up/down moving distances. With the gait data of 60 subjects including 20 hemiplegia patients and 40 healthy subjects, we employ a random forest-based classification approach to analyze the importances of different gait features for hemiplegia classification. Thanks to the over-fitting avoidance nature of the random forest approach, we do not need to have a careful control over the percentage of patients in the training data. In our experiments, our approach obtained the averaged classification accuracy of 90.65% among all the combinations of the gait features, which substantially outperformed state-of-the-art methods. The best classification accuracy of our approach is 95.45%, which is superior than all existing methods. Additionally, our approach also correctly reveals the importance of different gait features for hemiplegia classification. Our random forest-based approach outperforms support vector machine-based method and the Bayesian-based method, and can effectively extract gait features of subjects with hemiplegia for the classification and analysis of hemiplegia. Graphical Abstract Random Forest based Classsification and Analysis of Hemiplegia Gait using Low-cost Depth Cameras. Left: Motion capture with MS Kinect; Top-right: Random Forest Classsification based on the extracted gait features; Bottom-right: Sensitivity and specificity evaluation of the proposed classification approach.