The harmonic ratio (HR), derived from Fourier analysis of trunk accelerations, has been described in various ways as a measure of walking smoothness, walking rhythmicity, or dynamic stability. There is increasing interest in applying the HR technique to investigate the impact of various pathologies on locomotion; however, explanation of the method has been limited. The aim hereis to present a clear description of the mathematical basis of HRs and an understanding of their interpretation. We present harmonic theory, the interpretation of the HR using sinusoidal signals, and an exampleusing actual trunk accelerations and harmonic analyses during limb-loading conditions. We suggest that the HR method may be better defined, not as a measure of rhythmicity or stability, but as a measure of step-to-step symmetry within a stride.

Gait accelerometry is a promising tool to assess human walking and reveal deteriorating gait characteristics in patients and can be a rich source of clinically relevant information about functional declines in older adults. Therefore, in this paper, we propose a comprehensive set of signal features that may be used to extract clinically valuable information from gait accelerometry signals. To achieve our goal, we collected tri-axial gait accelerometry signals from 35 adults 65 years of age and older. Fourteen subjects were healthy controls, ten participants were diagnosed with Parkinson’s disease and eleven participants were diagnosed with peripheral neuropathy. The data were collected while the participants walked on a treadmill at a preferred walking speed. Accelerometer signal features in time, frequency and time-frequency domains were extracted. The results of our analysis showed that some of the extracted features were able to differentiate between healthy and clinical populations. Signal features in all three domains were able to emphasize variability among different groups, and also revealed valuable information about variability of the signals between anterior-posterior, mediolateral and vertical directions within subjects. The current results imply that the proposed signal features can be valuable tools for the analysis of gait accelerometry data and should be utilized in future studies.

Background High-technology methods demonstrate that balance problems may persist up to 30 days after a concussion, whereas with low-technology methods such as the Balance Error Scoring System (BESS), performance becomes normal after only 3 days based on previously published studies in collegiate and high school athletes. Purpose To compare the National Institutes of Health’s Balance Accelerometer Measure (BAM) with the BESS regarding the ability to detect differences in postural sway between adolescents with sports concussions and age-matched controls. Study Design Cohort study (diagnosis); Level of evidence, 2. Methods Forty-three patients with concussions and 27 control participants were tested with the standard BAM protocol, while sway was quantified using the normalized path length (mG/s) of pelvic accelerations in the anterior-posterior direction. The BESS was scored by experts using video recordings. Results The BAM was not able to discriminate between healthy and concussed adolescents, whereas the BESS, especially the tandem stance conditions, was good at discriminating between healthy and concussed adolescents. A total BESS score of 21 or more errors optimally identified patients in the acute concussion group versus healthy participants at 60% sensitivity and 82% specificity. Conclusion The BAM is not as effective as the BESS in identifying abnormal postural control in adolescents with sports concussions. The BESS, a simple and economical method of assessing postural control, was effective in discriminating between young adults with acute concussions and young healthy people, suggesting that the test has value in the assessment of acute concussions.

Background: Accelerometers are being used to assess postural control in adults, but there is little to support their reliability and validity. Objective: To estimate the test-retest reliability of the balance accelerometry measure (BAM) and to describe the known-groups validity of the BAM composite score. Methods: Two measures of standing postural sway were taken across six sensory (vision/stance surface) and motor stance (feet together or tandem) positions from eighteen patients with vestibular disorders and 84 healthy subjects. Test-retest reliability for postural sway was estimated across all conditions using intraclass correlation coefficient (ICC). A composite measure of sway standardized to young healthy subjects on eyes open firm surface stance was compared between groups. Results: Test-retest reliability of postural sway was good (ICC ⩾ 0.74) under all sensory conditions except eyes closed/tandem stance, which was slight to poor. Analysis of the receiver operating characteristic curve for composite scores indicated significant accuracy at identification of subjects in the vestibular/balance disorder groups. Composite standard scores equal or greater than 21.1 identified subjects with vestibular disorders with an accuracy of 72% sensitivity and 68% specificity. Conclusion: The BAM displays good-excellent reliability for five of six sensory-motor conditions. The composite score appears to differentiate healthy from subjects with vestibular disorders.

Objective Evaluating stride events can be valuable for understanding the changes in walking due to aging and neurological diseases. However, creating the time series necessary for this analysis can be cumbersome. In particular, finding heel contact and toe-off events which define the gait cycles accurately are difficult. Method We proposed a method to extract stride cycle events from tri-axial accelerometry signals. We validated our method via data collected from 14 healthy controls, 10 participants with Parkinson’s disease and 11 participants with peripheral neuropathy. All participants walked at self-selected comfortable and reduced speeds on a computer-controlled treadmill. Gait accelerometry signals were captured via a tri-axial accelerometer positioned over the L3 segment of the lumbar spine. Motion capture data were also collected and served as the comparison method. Results Our analysis of the accelerometry data showed that the proposed methodology was able to accurately extract heel and toe contact events from both feet. We used t-tests, ANOVA and mixed models to summarize results and make comparisons. Mean gait cycle intervals were the same as those derived from motion capture and cycle-to-cycle variability measures were within 1.5%. Subject group differences could be identified similarly using measures with the two methods. Conclusions A simple tri-axial acceleromter accompanied by a signal processing algorithm can be used to capture stride events. Clinical Impact: The proposed algorithm enables the assessment of stride events during treadmill walking, and is the first step towards the assessment of stride events using tri-axial accelerometers in real-life settings.

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