Gender differences in gait kinematics for patients with knee osteoarthritis

BACKGROUND: Females have a two-fold risk of developing knee osteoarthritis (OA) as compared to their male counterparts and atypical walking gait biomechanics are also considered a factor in the aetiology of knee OA. However, few studies have investigated sex-related differences in walking mechanics for patients with knee OA and of those, conflicting results have been reported. Therefore, this study was designed to examine the differences in gait kinematics (1) between male and female subjects with and without knee OA and (2) between healthy gender-matched subjects as compared with their OA counterparts. METHODS: One hundred subjects with knee OA (45 males and 55 females) and 43 healthy subjects (18 males and 25 females) participated in this study. Three-dimensional kinematic data were collected during treadmill-walking and analysed using (1) a traditional approach based on discrete variables and (2) a machine learning approach based on principal component analysis (PCA) and support vector machine (SVM) using waveform data. RESULTS: OA and healthy females exhibited significantly greater knee abduction and hip adduction angles compared to their male counterparts. No significant differences were found in any discrete gait kinematic variable between OA and healthy subjects in either the male or female group. Using PCA and SVM approaches, classification accuracies of 98–100 % were found between gender groups as well as between OA groups. CONCLUSIONS: These results suggest that care should be taken to account for gender when investigating the biomechanical aetiology of knee OA and that gender-specific analysis and rehabilitation protocols should be developed

Translating technology to clinical practice: Predicting how knee osteoarthritis patients will respond to an exercise intervention

Muscle strengthening exercises consistently demonstrate improvements in the pain and function of adults with knee osteoarthritis, but individual response rates can vary greatly. Identifying individuals who are more likely to respond is important in developing more efficient rehabilitation programs for knee osteoarthritis. Therefore, the overall goal of this thesis was to identify responders to exercise with a conventional motion capture system and translate these findings into a clinically accessible wearable sensor system. It was found that a conventional motion capture system, in combination with patient-reported outcome measures (e.g., function) collected at the baseline of an exercise intervention can successfully predict responders to treatment with greater than 85% accuracy (chapter three). To translate these findings to the clinical setting, more accessible wearable sensors (e.g., accelerometers) were examined in the remaining chapters. Chapter four found that while a single sensor at the lower back could subgroup some gait patterns, it was not sensitive enough to separate other, more similar, gait patterns. Therefore, the reliability of using multiple wearable sensors was examined in chapter five. The lower back, thigh, shank, and foot were all found to be reliable sensor locations for gait analysis and therefore suitable in the final study as potential predictors of response. Finally, chapter six found that a unique combination of wearable sensor data and patient reported outcome measures could successfully identify responders to an exercise intervention with similar accuracy to the conventional motion capture system. Further, the best limited set of sensors included only the back and thigh. Therefore, these findings suggest the potential development of a simplified two sensor system that can provide clinicians with an efficient and relatively unobtrusive way to use to optimize treatment

Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for “Code Reuse” to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease

Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach

Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns related to improvements in clinical outcomes. Eight patients with knee osteoarthritis (OA) completed two gait trials before and one following an exercise intervention. Wearable sensor data (e.g., 3-dimensional (3D) linear accelerations) were collected from a sensor located near the lower back, lateral thigh and lateral shank during level treadmill walking at a preferred speed. Wearable sensor data from the 2 pre-intervention gait trials were used to define each individual’s typical movement pattern using a one-class support vector machine (OCSVM). The percentage of strides defined as outliers, based on the pre-intervention gait data and the OCSVM, were used to define the overall change in an individual’s movement pattern. The correlation between the change in movement patterns following the intervention (i.e., percentage of outliers) and improvement in self-reported clinical outcomes (e.g., pain and function) was assessed using a Spearman rank correlation. The number of outliers observed post-intervention exhibited a large association (ρ = 0.78) with improvements in self-reported clinical outcomes. These findings demonstrate a proof-of-concept and a novel methodological approach for integrating machine learning and wearable sensor data. This approach provides an objective and evidence-informed way to understand clinically important changes in human movement patterns in response to exercise therapy

The measurement of stride-to-stride fluctuations in the gait of young and older adults using a body-fixed, tri-axial accelerometer.

A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Kinesiology & Health Studies, University of Regina. vii, 82 l.Falls represent one of the most significant health problems that affect older adults. Having the ability to accurately analyze and screen the gait of individuals who may be at risk of falling is an essential step to improving their health. Research has shown that there are numerous age-related changes in gait and that these changes may increase the risk of falling. More important than mean spatiotemporal parameters of gait, are the stride-to-stride fluctuations inherent in these measures. Both gait variability (i.e., the standard deviation of spatiotemporal gait parameters) and fractal dynamics (i.e., the patterning of fluctuations observed over a larger number of strides) can be useful in understanding the motor control of gait and predicting those who are at risk of falling. Combining this knowledge of gait variability and fractal dynamics with a simple and accurate tool could have a great impact on the effectiveness of diagnosing and treating older adults who are at risk of falling. While many devices are used to study such gait parameters, few are more intriguing than portable body-fixed, accelerometers. The small size and refined accuracy of today’s accelerometers make them excellent for analyzing gait. There is little research to confirm the validity of the accelerometer in simple mean spatiotemporal gait parameters such as stride time, and little to no research regarding more complicated measures of gait variability and fractal dynamics. The purpose of this study was to determine the validity of a body-fixed, tri-axial accelerometer in assessing mean, variability, and fractal measures of gait and to assess whether this device was sensitive enough to discriminate between healthy young and healthy older adults. Two accelerometer processing methods (e.g., vertical accelerations and anteroposterior accelerations) were compared to a criterion device (footswitch) on these measures. The accelerometer was found to be highly valid on mean temporal parameters, as well as measures of gait variability and fractal dynamics in stride times. Lower levels of validity were observed in measures of gait variability and fractal dynamics in step times, with the anteroposterior method displaying a slight advantage over the vertical method. Nevertheless, both accelerometer methods, along with the footswitch, found older adults displayed significantly lower fractal scaling than younger adults, suggesting a more random gait pattern and a declining motor control of gait. This study supports the tri-axial accelerometer for gait analysis in older adults and those who may be at risk of falling, but cautions against the use of gait variability in combined step times.Studentye