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Background: Impairments of gait and balance often progress through the course of dementia, and are associated with increased risk of falls. Summary: This systematic review provides a critical analysis of the evidence linking quantitative measures of gait and balance to fall risk in older adults with dementia. Various instrumented measures of gait and postural stability including gait speed and non-instrumented performance measures including Timed Up and Go were shown to be capable of distinguishing fallers from non-fallers. Key Messages: Existing reviews indicate that impairments of gait and balance are associated with increased risk of falls in cognitively intact older people. There are inconsistencies, however, regarding the characteristics most predictive of a fall. In order to advance fall prevention efforts, there is an important need to understand the relationship between gait, balance, and fall risk, particularly in high-risk populations such as individuals with dementia.

Robotic stroke rehabilitation therapy can greatly increase the efficiency of therapy delivery. However, when left unsupervised, users often compensate for limitations in affected muscles and joints by recruiting unaffected muscles and joints, leading to undesirable rehabilitation outcomes. This paper aims to develop a computer vision system that augments robotic stroke rehabilitation therapy by automatically detecting such compensatory motions. Nine stroke survivors and ten healthy adults participated in this study. All participants completed scripted motions using a table-top rehabilitation robot. The healthy participants also simulated three types of compensatory motions. The 3-D trajectories of upper body joint positions tracked over time were used for multiclass classification of postures. A support vector machine (SVM) classifier detected lean-forward compensation from healthy participants with excellent accuracy (AUC = 0.98, F1 = 0.82), followed by trunk-rotation compensation (AUC = 0.77, F1 = 0.57). Shoulder-elevation compensation was not well detected (AUC = 0.66, F1 = 0.07). A recurrent neural network (RNN) classifier, which encodes the temporal dependency of video frames, obtained similar results. In contrast, F1-scores in stroke survivors were low for all three compensations while using RNN: lean-forward compensation (AUC = 0.77, F1 = 0.17), trunk-rotation compensation (AUC = 0.81, F1 = 0.27), and shoulder-elevation compensation (AUC = 0.27, F1 = 0.07). The result was similar while using SVM. To improve detection accuracy for stroke survivors, future work should focus on predefining the range of motion, direct camera placement, delivering exercise intensity tantamount to that of real stroke therapies, adjusting seat height, and recording full therapy sessions.

Background Gait impairments contribute to falls in people with dementia. In this study, we used a vision-based system to record episodes of walking over a 2-week period as participants moved naturally around their environment, and from these calculated spatiotemporal, stability, symmetry, and acceleration gait features. The aim of this study was to determine whether features of gait extracted from a vision-based system are associated with falls, and which of these features are most strongly associated with falling. Methods Fifty-two people with dementia admitted to a specialized dementia unit participated in this study. Thirty different features describing baseline gait were extracted from Kinect recordings of natural gait over a 2-week period. Baseline clinical and demographic measures were collected, and falls were tracked throughout the participants’ admission. Results A total of 1,744 gait episodes were recorded (mean 33.5 ± 23.0 per participant) over a 2-week baseline period. There were a total of 78 falls during the study period (range 0–10). In single variable analyses, the estimated lateral margin of stability, step width, and step time variability were significantly associated with the number of falls during admission. In a multivariate model controlling for clinical and demographic variables, the estimated lateral margin of stability (p = .01) was remained associated with number of falls. Conclusions Information about gait can be extracted from vision-based recordings of natural walking. In particular, the lateral margin of stability, a measure of lateral gait stability, is an important marker of short-term falls risk.

This paper integrates an unobtrusive and affordable sensing technology with machine learning methods to discriminate between healthy and pathological gait patterns as a result of stroke or acquired brain injury. A feature analysis is used to identify the role of each body part in separating pathological patterns from healthy patterns. Gait features, including the orientations of the hips and spine (trunk), shoulders and neck (upper limb), knees and ankles (lower limb), are calculated during walking based on Kinect skeletal tracking sequences. Sequences of these features during three types of walking conditions were examined: 1) walking at self-pace (WSP); 2) walking at distracted (WD); and 3) walking at fast pace (WFP). Two machine learning approaches, an instance-based discriminative classifier ( -nearest neighbor) and a dynamical generative classifier (using Gaussian Process Latent Variable Model), are examined to distinguish between healthy and pathological gaits. Nested cross validation is implemented to evaluate the performance of the two classifiers using three metrics: F1-score, macro-averaged error, and micro-averaged error. The discriminative model outperforms the generative model in terms of the F1-score (discriminative: WSP> 0.95, WD > 0.96, and WFP > 0.95 and generative: WSP > 0.87, WD > 0.85, and WFP > 0.68) and macro-averaged error (discriminative: WSP < 0.08, WD < 0.1, and WFP < 0.09 and generative: WSP < 0.11, WD < 0.12, and WFP < 0.14). The dynamical generative model on the other hand obtains better micro-averaged error (discriminative: WSP < 0.37, WD < 0.3, and WFP < 0.35 and generative: WSP < 0.15, WD < 0.2, and WFP < 0.2). The high-dimensional gait features are divided into five subsets: lower limb, upper limb, trunk, velocity, and acceleration. An instance-based feature analysis method (ReliefF) is used to assign weights to each subset of features according to its discriminatory power. The feature analysis establishes the most informative features (upper limb, lower limb, and trunk) for identifying pathological gait.

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This study applies mixture-model clustering to spatiotemporal gait parameters in order to characterize the pathological gait pattern and to generate a composite measure indicative of overall gait performance. Gait data from 68 adults with stroke (age: 61.5 ± 13.6 years) and 20 healthy adults (age: 28.8 ± 7.1 years) were used in this study. Participants performed three passes across a GAITRite mat at different time points following stroke (poststroke adults only). Mixture-model clustering grouped participants' gait patterns based on their spatiotemporal gait features including symmetry, speed, and variability. Mixture-models with different covariance matrix parameterizations and numbers of clusters were examined. The selected clustering model successfully categorized participants' spatiotemporal gait data into three clinically meaningful groups. Based on the clustering results, gait speed, and variability measures varied across the three groups. Individuals in Group 1 are all symmetric and had the fastest and lowest gait velocity and variability, respectively. As expected, healthy participants were assigned to Group 1. All gait parameters were at an intermediate level in Group 2 and worse condition in Group 3. Moreover, resulting cluster centers were in line with previously published clinical studies on gait. In addition to clustering, each individual was given an indexed membership (ranged 0-1) to each of three groups. These indexed memberships were proposed as a single measure to encompass information about multiple gait parameters (symmetry, speed, and variability) and as a measure that is sensitive and responsive to improvement or deterioration and rehabilitation over time.