Estimating fall risk with inertial sensors using gait stability measures that do not require step detection

The Effects of Aging and Dual Tasking on Human Gait Complexity During Treadmill Walking: A Comparative Study Using Quantized Dynamical Entropy and Sample Entropy

Quantized dynamical entropy (QDE) has recently been proposed as a new measure to quantify the complexity of dynamical systems with the purpose of offering a better computational efficiency. This paper further investigates the viability of this method using five different human gait signals. These signals are recorded while normal walking and while performing secondary tasks among two age groups (young and older age groups). The results are compared with the outcomes of previously established sample entropy (SampEn) measure for the same signals. We also study how analyzing segmented and spatially and temporally normalized signal differs from analyzing whole data. Our findings show that human gait signals become more complex as people age and while they are cognitively loaded. Center of pressure (COP) displacement in mediolateral direction is the best signal for showing the gait changes. Moreover, the results suggest that by segmenting data, more information about intrastride dynamical features are obtained. Most importantly, QDE is shown to be a reliable measure for human gait complexity analysis.

Effects of backpack load and positioning on nonlinear gait features in young adults

Overloaded backpacks can cause changes in posture and gait dynamic balance. Therefore, the aim of this study was to assess gait regularity and local dynamic stability in young adults as they carried a backpack in different positions, and with different loads. Twenty-one healthy young adults participated in the study, carrying a backpack that was loaded with 10 and 20% of their body weight (BW). The participants walked on a level treadmill at their preferred walking speeds for 4 min under different conditions of backpack load and position (i.e. with backpack positioned back bilaterally, back unilaterally, frontally or without a backpack). Results indicate that backpack load and positioning significantly influence gait stability and regularity, with the exception of the 10% BW bilateral back position. Therefore, the recommended safe load for school-age children and adolescents (10% of BW) should also be considered for young adults. Practitioner summary: Increase in load results in changes in posture, muscle activity and gait parameters, so we investigated the gait adaptations related to regularity and stability. Conditions with high backpack loads significantly influenced gait stability and regularity in a position-dependent manner, except for 10% body weight bilateral back position.

Self-perceived gait stability modulates the effect of daily life gait quality on prospective falls in older adults

BACKGROUND

Quality of gait during daily life activities and perceived gait stability are both independent risk factors for future falls in older adults.

RESEARCH QUESTION

We investigated whether perceived gait stability modulates the association between gait quality and falling in older adults.

METHODS

In this prospective cohort study, we used one-week daily-life trunk acceleration data of 272 adults over 65 years of age. Sample entropy (SE) of the 3D acceleration signals was calculated to quantify daily life gait quality. To quantify perceived gait stability, the level of concern about falling was assessed using the Falls Efficacy Scale international (FES-I) questionnaire and step length, estimated from the accelerometer data. A fall calendar was used to record fall incidence during a six-month follow up period. Logistic regression analyses were performed to study the association between falling and SE, step length or FES-I score, and their interactions.

RESULTS

High (i.e., poor) SE in vertical direction was significantly associated with falling. FES-I scores significantly modulated this association, whereas step length did not. Subgroup analyses based on FES-I scores showed that high SE in the vertical direction was a risk factor for falls only in older adults who had a high (i.e. poor) FES-I score. In conclusion, perceived gait stability modulates the association between gait quality and falls in older adults such that an association between gait quality and falling is only present when perceived gait stability is poor.

SIGNIFICANCE

The results of the present study indicate that the effectiveness of interventions for fall prevention, aimed at improving gait quality, may be affected by a modulating effect of perceived gait stability. Results indicate that interventions to reduce falls in older adults might sort most effectiveness in populations with both a poor physiological and psychological status.

Improved Prediction of Falls in Community-Dwelling Older Adults Through Phase-Dependent Entropy of Daily-Life Walking

Age and age-related diseases have been suggested to decrease entropy of human gait kinematics, which is thought to make older adults more susceptible to falls. In this study we introduce a new entropy measure, called phase-dependent generalized multiscale entropy (PGME), and test whether this measure improves fall-risk prediction in community-dwelling older adults. PGME can assess phase-dependent changes in the stability of gait dynamics that result from kinematic changes in events such as heel strike and toe-off. PGME was assessed for trunk acceleration of 30 s walking epochs in a re-analysis of 1 week of daily-life activity data from the FARAO study, originally described by van Schooten et al. (2016). The re-analyzed data set contained inertial sensor data from 52 single- and 46 multiple-time prospective fallers in a 6 months follow-up period, and an equal number of non-falling controls matched by age, weight, height, gender, and the use of walking aids. The predictive ability of PGME for falls was assessed using a partial least squares regression. PGME had a superior predictive ability of falls among single-time prospective fallers when compared to the other gait features. The single-time fallers had a higher PGME (p < 0.0001) of their trunk acceleration at 60% of their step cycle when compared with non-fallers. No significant differences were found between PGME of multiple-time fallers and non-fallers, but PGME was found to improve the prediction model of multiple-time fallers when combined with other gait features. These findings suggest that taking into account phase-dependent changes in the stability of the gait dynamics has additional value for predicting falls in older people, especially for single-time prospective fallers.

Novel sensing technology in fall risk assessment in older adults: a systematic review

Background

Falls are a major health problem for older adults with significant physical and psychological consequences. A first step of successful fall prevention is to identify those at risk of falling. Recent advancement in sensing technology offers the possibility of objective, low-cost and easy-to-implement fall risk assessment. The objective of this systematic review is to assess the current state of sensing technology on providing objective fall risk assessment in older adults.

Methods

A systematic review was conducted in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement (PRISMA).

Results

Twenty-two studies out of 855 articles were systematically identified and included in this review. Pertinent methodological features (sensing technique, assessment activities, outcome variables, and fall discrimination/prediction models) were extracted from each article. Four major sensing technologies (inertial sensors, video/depth camera, pressure sensing platform and laser sensing) were reported to provide accurate fall risk diagnostic in older adults. Steady state walking, static/dynamic balance, and functional mobility were used as the assessment activity. A diverse range of diagnostic accuracy across studies (47.9% - 100%) were reported, due to variation in measured kinematic/kinetic parameters and modelling techniques.

Conclusions

A wide range of sensor technologies have been utilized in fall risk assessment in older adults. Overall, these devices have the potential to provide an accurate, inexpensive, and easy-to-implement fall risk assessment. However, the variation in measured parameters, assessment tools, sensor sites, movement tasks, and modelling techniques, precludes a firm conclusion on their ability to predict future falls. Future work is needed to determine a clinical meaningful and easy to interpret fall risk diagnosis utilizing sensing technology. Additionally, the gap between functional evaluation and user experience to technology should be addressed.

Towards an objective assessment of motor function in sub-acute stroke patients: Relationship between clinical rating scales and instrumental gait stability indexes

The assessment of walking function alterations is a key issue to design effective rehabilitative interventions in sub-acute stroke patients. Nevertheless, the objective quantification of these alterations remains a challenge. Clinical rating scales are commonly used in clinical practice, but have been proven prone to errors associated to the evaluator subjective perception. On the other hand, instrumental measurement of trunk acceleration can be exploited for an objective quantitative characterization of gait function, but it is not applied in routine clinical practice, because the resulting quantitative indexes have not been related to the clinically information, conventionally provided by the rating scales. To overcome this limitation, the relationship between the indexes, in specific clinical conditions, and rating scale must be better investigated, to support their exploitability in the clinical practice as a fast and reliable screening tool. Thirty-one sub-acute stroke patients (17 with and 14 without cane) participated in the study. All were assessed with 6 rating scales (MI, TCT, MRI, FAC, WHS, CIRS) and 2 functional tests (2MWT and TUG). Sample Entropy (SEN) and Recurrence Quantification Analysis (RQA) in AP, ML and V directions were calculated over 2MWT and walking section of TUG. The influence of assessment task and cane was analysed, as well as correlation of SEN and RQA indexes with clinical rating scales. SEN and RQA on the medio-lateral plane resulted influenced by the use of the cane, while the correlations between indexes and clinical scales showed that SEN and RQA for antero-posterior direction correlate positively with WHS.

Step-to-Step Ankle Inversion/Eversion Torque Modulation Can Reduce Effort Associated with Balance

Below-knee amputation is associated with higher energy expenditure during walking, partially due to difficulty maintaining balance. We previously found that once-per-step push-off work control can reduce balance-related effort, both in simulation and in experiments with human participants. Simulations also suggested that changing ankle inversion/eversion torque on each step, in response to changes in body state, could assist with balance. In this study, we investigated the effects of ankle inversion/eversion torque modulation on balance-related effort among amputees (N = 5) using a multi-actuated ankle-foot prosthesis emulator. In stabilizing conditions, changes in ankle inversion/eversion torque were applied so as to counteract deviations in side-to-side center-of-mass acceleration at the moment of intact-limb toe off; higher acceleration toward the prosthetic limb resulted in a corrective ankle inversion torque during the ensuing stance phase. Destabilizing controllers had the opposite effect, and a zero gain controller made no changes to the nominal inversion/eversion torque. To separate the balance-related effects of step-to-step control from the potential effects of changes in average mechanics, average ankle inversion/eversion torque and prosthesis work were held constant across conditions. High-gain stabilizing control lowered metabolic cost by 13% compared to the zero gain controller (p = 0.05). We then investigated individual responses to subject-specific stabilizing controllers following an enforced exploration period. Four of five participants experienced reduced metabolic rate compared to the zero gain controller (−15, −14, −11, −6, and +4%) an average reduction of 9% (p = 0.05). Average prosthesis mechanics were unchanged across all conditions, suggesting that improvements in energy economy might have come from changes in step-to-step corrections related to balance. Step-to-step modulation of inversion/eversion torque could be used in new, active ankle-foot prostheses to reduce walking effort associated with maintaining balance.

Gait parameters are differently affected by concurrent smartphone-based activities with scaled levels of cognitive effort

The widespread and pervasive use of smartphones for sending messages, calling, and entertainment purposes, mainly among young adults, is often accompanied by the concurrent execution of other tasks. Recent studies have analyzed how texting, reading or calling while walking–in some specific conditions–might significantly influence gait parameters. The aim of this study is to examine the effect of different smartphone activities on walking, evaluating the variations of several gait parameters. 10 young healthy students (all smartphone proficient users) were instructed to text chat (with two different levels of cognitive load), call, surf on a social network or play with a math game while walking in a real-life outdoor setting. Each of these activities is characterized by a different cognitive load. Using an inertial measurement unit on the lower trunk, spatio-temporal gait parameters, together with regularity, symmetry and smoothness parameters, were extracted and grouped for comparison among normal walking and different dual task demands. An overall significant effect of task type on the aforementioned parameters group was observed. The alterations in gait parameters vary as a function of cognitive effort. In particular, stride frequency, step length and gait speed show a decrement, while step time increases as a function of cognitive effort. Smoothness, regularity and symmetry parameters are significantly altered for specific dual task conditions, mainly along the mediolateral direction. These results may lead to a better understanding of the possible risks related to walking and concurrent smartphone use.

Gait characteristics and their discriminative power in geriatric patients with and without cognitive impairment

BACKGROUND

A detailed gait analysis (e.g., measures related to speed, self-affinity, stability, and variability) can help to unravel the underlying causes of gait dysfunction, and identify cognitive impairment. However, because geriatric patients present with multiple conditions that also affect gait, results from healthy old adults cannot easily be extrapolated to geriatric patients. Hence, we (1) quantified gait outcomes based on dynamical systems theory, and (2) determined their discriminative power in three groups: healthy old adults, geriatric patients with- and geriatric patients without cognitive impairment.

METHODS

For the present cross-sectional study, 25 healthy old adults recruited from community (65 ± 5.5 years), and 70 geriatric patients with (n = 39) and without (n = 31) cognitive impairment from the geriatric dayclinic of the MC Slotervaart hospital in Amsterdam (80 ± 6.6 years) were included. Participants walked for 3 min during single- and dual-tasking at self-selected speed while 3D trunk accelerations were registered with an IPod touch G4. We quantified 23 gait outcomes that reflect multiple gait aspects. A multivariate model was built using Partial Least Square- Discriminant Analysis (PLS-DA) that best modelled participant group from gait outcomes.

RESULTS

For single-task walking, the PLS-DA model consisted of 4 Latent Variables that explained 63 and 41% of the variance in gait outcomes and group, respectively. Outcomes related to speed, regularity, predictability, and stability of trunk accelerations revealed with the highest discriminative power (VIP > 1). A high proportion of healthy old adults (96 and 93% for single- and dual-task, respectively) was correctly classified based on the gait outcomes. The discrimination of geriatric patients with and without cognitive impairment was poor, with 57% (single-task) and 64% (dual-task) of the patients misclassified.

CONCLUSIONS

While geriatric patients vs. healthy old adults walked slower, and less regular, predictable, and stable, we found no differences in gait between geriatric patients with and without cognitive impairment. The effects of multiple comorbidities on geriatric patients' gait possibly causes a 'floor-effect', with no room for further deterioration when patients develop cognitive impairment. An accurate identification of cognitive status thus necessitates a multifactorial approach.

Examination of the gait pattern based on adjusting and resulting components of the stride-to-stride variability: proof of concept

Background

Stride-to-stride variability may be used as an indicator in the assessment of gait performance, but the evaluation of this parameter is not trivial. In the gait pattern, a deviation in one stride must be corrected within the next strides (elemental variables) to ensure a steady gait (performance variable). The variance in these elemental and performance variables may therefore be evaluated as adjusting and resulting components of variability. We explored this approach to gait evaluation by matching the velocity of one stride to a subsequent stride with four different time lags ranging from 0.5 to 2 strides with 0.5 stride increments. The time lag values corresponded to the following contralateral stride, the following ipsilateral stride, the second following contralateral stride and the second following ipsilateral stride.

Methods

Twenty asymptomatic young adults walked on an instrumented treadmill at their preferred gait speed. The stride velocity was calculated, and variances in the stride-to-stride differences and in the stride-to-stride sums represented the adjusting and the resulting variances, respectively. A ratio between these values of greater than one indicated a meaningful stride-to-stride interaction.

Results

For the four time lags (0.5, 1, 1.5, and 2 strides), the adjusting/resulting variance ratios (mean and CI 95%) were 1.0 (0.8–1.2), 2.9 (2.3–3.6), 1.2 (1.0–1.4) and 1.2 (0.9–1.4), respectively.

Conclusions

This new approach to the evaluation of stride-to-stride variability suggests that gait velocity adjustments occurred within one full stride cycle during treadmill walking among asymptomatic young adults. The validity of the approach needs to be tested in over-ground walking.

A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future.

Gait dynamics to optimize fall risk assessment in geriatric patients admitted to an outpatient diagnostic clinic

Fall prediction in geriatric patients remains challenging because the increased fall risk involves multiple, interrelated factors caused by natural aging and/or pathology. Therefore, we used a multi-factorial statistical approach to model categories of modifiable fall risk factors among geriatric patients to identify fallers with highest sensitivity and specificity with a focus on gait performance. Patients (n = 61, age = 79; 41% fallers) underwent extensive screening in three categories: (1) patient characteristics (e.g., handgrip strength, medication use, osteoporosis-related factors) (2) cognitive function (global cognition, memory, executive function), and (3) gait performance (speed-related and dynamic outcomes assessed by tri-axial trunk accelerometry). Falls were registered prospectively (mean follow-up 8.6 months) and one year retrospectively. Principal Component Analysis (PCA) on 11 gait variables was performed to determine underlying gait properties. Three fall-classification models were then built using Partial Least Squares-Discriminant Analysis (PLS-DA), with separate and combined analyses of the fall risk factors. PCA identified 'pace', 'variability', and 'coordination' as key properties of gait. The best PLS-DA model produced a fall classification accuracy of AUC = 0.93. The specificity of the model using patient characteristics was 60% but reached 80% when cognitive and gait outcomes were added. The inclusion of cognition and gait dynamics in fall classification models reduced misclassification. We therefore recommend assessing geriatric patients' fall risk using a multi-factorial approach that incorporates patient characteristics, cognition, and gait dynamics.

Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study

Falls are a major health problem for older adults with immediate effects, such as fractures and head injuries, and longer term effects including fear of falling, loss of independence, and disability. The goals of the WIISEL project were to develop an unobtrusive, self-learning and wearable system aimed at assessing gait impairments and fall risk of older adults in the home setting; assessing activity and mobility in daily living conditions; identifying decline in mobility performance and detecting falls in the home setting. The WIISEL system was based on a pair of electronic insoles, able to transfer data to a commercially available smartphone, which was used to wirelessly collect data in real time from the insoles and transfer it to a backend computer server via mobile internet connection and then onwards to a gait analysis tool. Risk of falls was calculated by the system using a novel Fall Risk Index (FRI) based on multiple gait parameters and gait pattern recognition. The system was tested by twenty-nine older users and data collected by the insoles were compared with standardized functional tests with a concurrent validity approach. The results showed that the FRI captures the risk of falls with accuracy that is similar to that of conventional performance-based tests of fall risk. These preliminary findings support the idea that theWIISEL system can be a useful research tool and may have clinical utility for long-term monitoring of fall risk at home and in the community setting.

Feature selection for elderly faller classification based on wearable sensors

BACKGROUND

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data.

METHODS

A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling.

RESULTS

The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier.

CONCLUSIONS

Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Alterations in over-ground walking patterns in obese and overweight adults

Obesity has been associated with negative effects on postural control, including falls. Previous studies revealed different outcomes regarding the effects of obesity on gait features, and the use of BMI may lead to bias in assessing the true effects of obesity on gait. To better understand the effects of obesity on gait, it is important to examine gait features and associated body composition measures. The purpose of this study was: (1) to assess gait features of normal weight, overweight and obese adults, and (2) to assess the relationship between body composition measures and gait features. Thirty participants were assigned to one of three groups based upon their BMI at the onset of the study: healthy weight (BMI: 18.5-24.9kg/m²), overweight (BMI: 25-29.9kg/m²), or obese (BMI: 30-40kg/m²). Participants performed straight-line over-ground walking through a 200m hallway at their natural preferred speed while wearing their own shoes. The angular displacements, range of motion (ROM), and approximate entropy of kinematic data of the bilateral hips, knees, and ankles in the sagittal plane were computed. Walking speed, step length, stride length, single leg support phase, double leg support phase, swing phase and bilateral stance phase times were extracted from the GaitRite data. Overall, body mass and BMI were associated with peak flexion and ROM in the knees as well as single support, double support, stance, and swing phases. Body fat percentage did not exhibit correlations with measured gait features. Gait variables were more highly correlated with BMI and body mass instead of percent body fat, suggesting that absolute mass is more influential on gait features rather than amount of fat tissue.

Multiscale and Shannon entropies during gait as fall risk predictors-A prospective study

Although entropy-based measurements of gait dynamics are becoming widely used tools for fall risk assessment, their relationship to fall occurrence is still unclear. The aim of this study was hence to compare fallers and non-fallers in terms of gait dynamics assessed by the multiscale and Shannon entropy. This study included 139 participants, aged 60-80 years, divided into two groups according to fall occurrence during a 6-month prospective observation (38 fallers, 101 non-fallers). The methodology involved the use of the Tinetti balance assessment tool (TBAT) and 5min of overground walking with 3D accelerometers located near the L5 vertebra and shanks. We analyzed 150 strides for gait complexity, an index of complexity (CI), computed from multiscale entropy (MSE) and Shannon entropy (ShE) derived from the recurrence quantification analysis. We found no significant differences between groups in MSE and CI. The TBAT total score was significantly higher in non-fallers (P=0.033), however, both groups showed low risk of falls. ShE in the anterior-posterior direction from trunk and in the medial-lateral direction from the shanks were both significantly higher in fallers (P=0.020; P=0.024). ShE was negatively correlated with CI, the shank ShE in the vertical direction was positively correlated with TBAT. Taken together, our findings suggest that MSE is not able to distinguish between highly functional groups, whereas Shannon entropy seems to be sufficient in fall risk prediction.

Is the Assessment of 5 Meters of Gait with a Single Body-Fixed-Sensor Enough to Recognize Idiopathic Parkinson's Disease-Associated Gait?

Quantitative assessment of gait in patients with Parkinson’s disease (PD) is an important step in addressing motor symptoms and improving clinical management. Based on the assessment of only 5 meters of gait with a single body-fixed-sensor placed on the lower back, this study presents a method for the identification of step-by-step kinematic parameters in 14 healthy controls and in 28 patients at early-to-moderate stages of idiopathic PD. Differences between groups in step-by-step kinematic parameters were evaluated to understand gait impairments in the PD group. Moreover, a discriminant model between groups was built from a subset of significant and independent parameters and based on a 10-fold cross-validated model. The discriminant model correctly classified a total of 89.5% participants with four kinematic parameters. The sensitivity of the model was 95.8% and the specificity 78.6%. The results indicate that the proposed method permitted to reasonably recognize idiopathic PD-associated gait from 5-m walking assessments. This motivates further investigation on the clinical utility of short episodes of gait assessment with body-fixed-sensors.

Complexity of human gait pattern at different ages assessed using multiscale entropy: From development to decline

Multiscale entropy (MSE) has been applied in biomechanics to evaluate gait stability during human gait and was found to be a promising method for evaluating fall risk in elderly and/or pathologic subjects. The hypothesis of this work is that gait complexity is a relevant parameter of gait development during life, decreasing from immature to mature gait and then increasing again during old age. In order to verify this hypothesis, MSE was applied on trunk acceleration data collected during gait of subjects of different ages: toddlers at the onset of walking, pre-scholar and scholar children, adolescents, young adults, adults and elderlies. MSE was estimated by calculating sample entropy (SEN) on raw unfiltered data of L5 acceleration along the three axes, using values of τ ranging from 1 to 6. In addition, other performance parameters (cadence, stride time variability and harmonic ratio) were evaluated. The results followed the hypothesized trend when MSE was applied on the vertical and/or anteroposterior axis of trunk acceleration: an age effect was found and adult SEN values were significantly different from children ones. From young adults to elderlies a slight increase in SEN values was shown although not statistically significant. While performance gait parameters showed adolescent gait similar to the one of adults, SEN highlighted that their gait maturation is not complete yet. In conclusion, present results suggest that the complexity of gait, evaluated on the sagittal plane, can be used as a characterizing parameter of the maturation of gait control.

Treadmill walking is not equivalent to overground walking for the study of walking smoothness and rhythmicity in older adults

Treadmills are appealing for gait studies, but some gait mechanics are disrupted during treadmill walking. The purpose of this study was to examine the effects of speed and treadmill walking on walking smoothness and rhythmicity of 40 men and women between the ages of 70-96 years. Gait smoothness was examined during overground (OG) and treadmill (TM) walking by calculating the harmonic ratio from linear accelerations measured at the level of the lumbar spine. Rhythmicity was quantified as the stride time standard deviation. TM walking was performed at two speeds: a speed matching the natural OG walk speed (TM-OG), and a preferred TM speed (PTM). A dual-task OG condition (OG-DT) was evaluated to determine if TM walking posed a similar cognitive challenge. Statistical analysis included a one-way Analysis of Variance with Bonferroni corrected post hoc comparisons and the Wilcoxon signed rank test for non-normally distributed variables. Average PTM speed was slower than OG. Compared to OG, those who could reach the TM-OG speed (74.3% of sample) exhibited improved ML smoothness and rhythmicity, and the slower PTM caused worsened vertical and AP smoothness, but did not affect rhythmicity. PTM disrupted smoothness and rhythmicity differently than the OG-DT condition, likely due to reduced speed. The use of treadmills for gait smoothness and rhythmicity studies in older adults is problematic; some participants will not achieve OG speed during TM walking, walking at the TM-OG speed artificially improves rhythmicity and ML smoothness, and walking at the slower PTM speed worsens vertical and AP gait smoothness.

A novel accelerometry-based algorithm for the detection of step durations over short episodes of gait in healthy elderly

Background

The assessment of short episodes of gait is clinically relevant and easily implemented, especially given limited space and time requirements. BFS (body-fixed-sensors) are small, lightweight and easy to wear sensors, which allow the assessment of gait at relative low cost and with low interference. Thus, the assessment with BFS of short episodes of gait, extracted from dailylife physical activity or measured in a standardised and supervised setting, may add value in the study of gait quality of the elderly. The aim of this study was to evaluate the accuracy of a novel algorithm based on acceleration signals recorded at different human locations (lower back and heels) for the detection of step durations over short episodes of gait in healthy elderly subjects.

Methods

Twenty healthy elderly subjects (73.7 ± 7.9 years old) walked twice a distance of 5 m, wearing a BFS on the lower back, and on the outside of each heel. Moreover, an optoelectronic three-dimensional (3D) motion tracking system was used to detect step durations. A novel algorithm is presented for the detection of step durations from low-back and heel acceleration signals separately. The accuracy of the algorithm was assessed by comparing absolute differences in step duration between the three methods: step detection from the optoelectronic 3D motion tracking system, step detection from the application of the novel algorithm to low-back accelerations, and step detection from the application of the novel algorithm to heel accelerations.

Results

The proposed algorithm successfully detected all the steps, without false positives and without false negatives. Absolute average differences in step duration within trials and across subjects were calculated for each comparison, between low-back accelerations and the optoelectronic system were on average 22.4 ± 7.6 ms (4.0 ± 1.3 % of average step duration), between heel accelerations and the optoelectronic system were on average 20.7 ± 11.8 ms (3.7 ± 1.9 %), and between low-back accelerations and heel accelerations were on average 27.8 ± 15.1 ms (4.9 ± 2.5 % of average step duration).

Conclusions

This study showed that the presented novel algorithm detects step durations over short episodes of gait in healthy elderly subjects with acceptable accuracy from low-back and heel accelerations, which provides opportunities to extract a range of gait parameters from short episodes of gait.

Fall-related gait characteristics on the treadmill and in daily life

Background

Body-worn sensors allow assessment of gait characteristics that are predictive of fall risk, both when measured during treadmill walking and in daily life. The present study aimed to assess differences as well as associations between fall-related gait characteristics measured on a treadmill and in daily life.

Methods

In a cross-sectional study, trunk accelerations of 18 older adults (72.3 ± 4.5 years) were recorded during walking on a treadmill (Dynaport Hybrid sensor) and during daily life (Dynaport MoveMonitor). A comprehensive set of 32 fall-risk-related gait characteristics was estimated and compared between both settings.

Results

For 25 gait characteristics, a systematic difference between treadmill and daily-life measurements was found. Gait was more variable, less symmetric, and less stable during daily life. Fourteen characteristics showed a significant correlation between treadmill and daily-life measurements, including stride time and regularity (0.48 < r < 0.73; p < 0.022). No correlation between treadmill and daily-life measurements was found for stride-time variability, acceleration range and sample entropy in vertical and mediolateral direction, gait symmetry in vertical direction, and stability estimated as the local divergence exponent by Rosenstein’s method in mediolateral direction (r < 0.16; p > 0.25).

Conclusions

Gait characteristics revealed less stable, less symmetric, and more variable gait during daily life than on a treadmill, yet about half of the characteristics were significantly correlated between conditions. These results suggest that daily-life gait analysis is sensitive to static personal factors (i.e., physical and cognitive capacity) as well as dynamic situational factors (i.e., behavior and environment), which may both represent determinants of fall risk.

Electronic supplementary material

The online version of this article (doi:10.1186/s12984-016-0118-9) contains supplementary material, which is available to authorized users.

Fall prevention intervention technologies: A conceptual framework and survey of the state of the art

In recent years, an ever increasing range of technology-based applications have been developed with the goal of assisting in the delivery of more effective and efficient fall prevention interventions. Whilst there have been a number of studies that have surveyed technologies for a particular sub-domain of fall prevention, there is no existing research which surveys the full spectrum of falls prevention interventions and characterises the range of technologies that have augmented this landscape. This study presents a conceptual framework and survey of the state of the art of technology-based fall prevention systems which is derived from a systematic template analysis of studies presented in contemporary research literature. The framework proposes four broad categories of fall prevention intervention system: Pre-fall prevention; Post-fall prevention; Fall injury prevention; Cross-fall prevention. Other categories include, Application type, Technology deployment platform, Information sources, Deployment environment, User interface type, and Collaborative function. After presenting the conceptual framework, a detailed survey of the state of the art is presented as a function of the proposed framework. A number of research challenges emerge as a result of surveying the research literature, which include a need for: new systems that focus on overcoming extrinsic falls risk factors; systems that support the environmental risk assessment process; systems that enable patients and practitioners to develop more collaborative relationships and engage in shared decision making during falls risk assessment and prevention activities. In response to these challenges, recommendations and future research directions are proposed to overcome each respective challenge.

Review: Are we stumbling in our quest to find the best predictor? Over-optimism in sensor-based models for predicting falls in older adults

The field of fall risk testing using wearable sensors is bustling with activity. In this Letter, the authors review publications which incorporated features extracted from sensor signals into statistical models intended to estimate fall risk or predict falls in older people. A review of these studies raises concerns that this body of literature is presenting over-optimistic results in light of small sample sizes, questionable modelling decisions and problematic validation methodologies (e.g. inherent problems with the overly-popular cross-validation technique, lack of external validation). There seem to be substantial issues in the feature selection process, whereby researchers select features before modelling begins based on their relation to the target, and either perform no validation or test the models on the same data used for their training. This, together with potential issues related to the large number of features and their correlations, inevitably leads to models with inflated accuracy that are unlikely to maintain their reported performance during everyday use in relevant populations. Indeed, the availability of rich sensor data and many analytical options provides intellectual and creative freedom for researchers, but should be treated with caution, and such pitfalls must be avoided if we desire to create generalisable prognostic tools of any clinical value.

Mediolateral balance and gait stability in older adults

Early detection of balance impairment is crucial to identify individuals who may benefit from interventions aimed to prevent falls, which is a major problem in aging societies. Since mediolateral balance deteriorates with aging, we proposed a mediolateral balance assessment (MELBA) tool that uses a CoM-tracking task of predictable sinusoidal and unpredictable multisine targets. This method has shown to be reliable and sensitive to aging effect, however, it is not known whether it can predict performance on common daily-life tasks such as walking. This study aimed to determine whether MELBA is an ecologically valid tool by correlating its outputs with a measure of mediolateral gait stability known to be predictive of falls. Nineteen community-dwelling older adults (72±5 years) tracked predictable and unpredictable target displacements at increasing frequencies with their CoM by shifting their weight sideward. Response delay (phase-shift) and amplitude difference (gain) between the CoM and target in the frequency domain were used to quantify performance. To assess gait stability, the local divergence exponent was calculated using mediolateral accelerations with an inertial sensor when walking on a treadmill (LDETR) and in daily-life (LDEDL) for one week. Pearson product-moment correlation analyses were performed to determine correlations between performance on MELBA tasks and LDE. Results show that phase-shift bandwidth for the predictable target (range above -90°) was significantly correlated with LDETR whereas phase-shift bandwidth for the unpredictable target was significantly correlated with LDEDL. In conclusion MELBA is an ecologically valid tool for mediolateral balance assessment in community-dwelling older adults who exhibit subtle balance impairments.

Role of body-worn movement monitor technology for balance and gait rehabilitation

This perspective article will discuss the potential role of body-worn movement monitors for balance and gait assessment and treatment in rehabilitation. Recent advances in inexpensive, wireless sensor technology and smart devices are resulting in an explosion of miniature, portable sensors that can quickly and accurately quantify body motion. Practical and useful movement monitoring systems are now becoming available. It is critical that therapists understand the potential advantages and limitations of such emerging technology. One important advantage of obtaining objective measures of balance and gait from body-worn sensors is impairment-level metrics characterizing how and why functional performance of balance and gait activities are impaired. Therapy can then be focused on the specific physiological reasons for difficulty in walking or balancing during specific tasks. A second advantage of using technology to measure balance and gait behavior is the increased sensitivity of the balance and gait measures to document mild disability and change with rehabilitation. A third advantage of measuring movement, such as postural sway and gait characteristics, with body-worn sensors is the opportunity for immediate biofeedback provided to patients that can focus attention and enhance performance. In the future, body-worn sensors may allow therapists to perform telerehabilitation to monitor compliance with home exercise programs and the quality of their natural mobility in the community. Therapists need technological systems that are quick to use and provide actionable information and useful reports for their patients and referring physicians. Therapists should look for systems that provide measures that have been validated with respect to gold standard accuracy and to clinically relevant outcomes such as fall risk and severity of disability.

Validation of balance-quality assessment using a modified bathroom scale

The balance quality tester (BQT), based on a standard electronic bathroom scale has been developed in order to assess balance quality. The BQT includes automatic detection of the person to be tested by means of an infrared detector and bluetooth communication capability for remote assessment when linked to a long-distance communication device such as a mobile phone. The BQT was compared to a standard force plate for validity and agreement. The two most widely reported parameters in balance literature, the area of the centre of pressure (COP) displacement and the velocity of the COP displacement, were compared for 12 subjects, each of whom was tested on ten occasions on each of the 2 days. No significant differences were observed between the BQT and the force plate for either of the two parameters. In addition a high level of agreement was observed between both devices. The BQT is a valid device for remote assessment of balance quality, and could provide a useful tool for long-term monitoring of people with balance problems, particularly during home monitoring.

Do extreme values of daily-life gait characteristics provide more information about fall risk than median values?

Background

Gait characteristics estimated from daily-life trunk accelerations reflect gait quality and are associated with fall incidence in older adults. While associations are based on median values of these gait characteristics, their extreme values may reflect either high-risk situations or steady-state gait and may thus be more informative in relation to fall risk.

Objective

The objective of this study was to improve fall-risk prediction models by examining whether the use of extreme values strengthens the associations with falls.

Methods

Trunk acceleration data (Dynaport MoveMonitor) were collected from 202 older adults over a full week. From all walking episodes, we estimated the median and, as reliable estimates of the extremes, the 10th and 90th percentiles of gait characteristics, all over 10-second epochs. In addition, the amount of daily activities was derived from the acceleration data, and participants completed fall-risk questionnaires. Participants were classified as fallers based on one or more falls during 6 months of follow-up. Univariate analyses were performed to investigate whether associations with falls were stronger for the extremes than for the medians. Subsequently, three fall-risk models were compared: (1) using questionnaire data only, (2) adding the amount of activities and medians of gait characteristics, and (3) using extreme values instead of medians in the case of stronger univariate associations of the extremes.

Results

Stronger associations were found for the extreme characteristics reflecting high regularity, low frequency variability, and low local instability in anterior-posterior direction, for high symmetry in all directions and for low entropy in anterior-posterior and vertical directions. The questionnaire-only model improved significantly by adding activities and gait characteristics’ medians. Replacing medians by extremes with stronger associations did improve the fall prediction model, but not significantly.

Conclusions

Associations were stronger for extreme values, indicating “high gait quality” situations (ie, 10th and 90th percentiles in case of positive and negative associations, respectively) and not for “low gait quality” situations. This suggests that gait characteristics during optimal performance gait provide more information about the risk of falling than high-risk situations. However, their added value over medians in prediction is limited.

Role of visual input in the control of dynamic balance: variability and instability of gait in treadmill walking while blindfolded

While vision obviously plays an essential role in orienting and obstacle avoidance, its role in the regulation of dynamic balance is not yet fully understood. The objective of this study was to assess dynamic stability while blindfolded, under optimal conditions that minimized the fear of falling. The hypothesis was that visual deprivation could be compensated for by using other sensory strategies to stabilize gait. One hundred healthy adults (aged 20-69 years) participated in the study. They were previously accustomed to blindfolded treadmill walking wearing a safety harness. Their preferred walking speeds (PWS) were assessed with eyes open (PWSEO) and with eyes closed (blindfolded, PWSEC). Three five-minute tests were performed: (A) normal walking at PWSEO, (B) blindfolded walking at PWSEC, and (C) normal walking at PWSEC. Trunk acceleration was measured with a lightweight inertial sensor. Dynamic stability was assessed by using (1) acceleration root mean square (RMS), which estimates the variability of the signal, and hence, the smoothness of the trunk movement and (2) local dynamic stability (LDS), which reflects the efficiency of the motor control to stabilize the trunk. Although walking at PWSEC with eyes open (comparing conditions A and C) had a slight impact on gait stability (relative difference: RMS +4 %, LDS -5 %), no destabilizing effect of visual deprivation (B vs. C, RMS -4 %, LDS -1 %) was observed. Therefore, it is concluded that when reassuring conditions are offered to individuals while walking, they are able to adopt alternative sensory strategies to control dynamic equilibrium without the help of vision.

New methods for fall risk prediction

PURPOSE OF REVIEW

Accidental falls are the leading cause of injury-related death and hospitalization in old age, with over one-third of the older adults experiencing at least one fall or more each year. Because of limited healthcare resources, regular objective fall risk assessments are not possible in the community on a large scale. New methods for fall prediction are necessary to identify and monitor those older people at high risk of falling who would benefit from participating in falls prevention programmes.

RECENT FINDINGS

Technological advances have enabled less expensive ways to quantify physical fall risk in clinical practice and in the homes of older people. Recently, several studies have demonstrated that sensor-based fall risk assessments of postural sway, functional mobility, stepping and walking can discriminate between fallers and nonfallers.

SUMMARY

Recent research has used low-cost, portable and objective measuring instruments to assess fall risk in older people. Future use of these technologies holds promise for assessing fall risk accurately in an unobtrusive manner in clinical and daily life settings.

Are gait variability and stability measures influenced by directional changes?

BACKGROUND

Many gait variability and stability measures have been proposed in the literature, with the aim to quantify gait impairment, degree of neuro-motor control and balance disorders in healthy and pathological subjects. These measures are often obtained from lower trunk acceleration data, typically acquired during rectilinear gait, but relevant experimental protocols and data processing techniques lack in standardization. Since directional changes represent an essential aspect of gait, the assessment of their influence on such measures is essential for standardization. In addition, their investigation is needed to evaluate the applicability of these measures in laboratory trials and in daily life activity analysis. A further methodological aspect to be standardized concerns the assessment of the sampling frequency, which could affect stability measures. The aim of the present study was hence to assess if gait variability and stability measures are affected by directional changes, and to evaluate the influence of sampling frequency of trunk acceleration data on the results.

METHODS

Fifty-one healthy young adults performed a 6-minute walk test along a 30 m straight pathway, turning by 180 deg at each end of the pathway. Nine variability and stability measures (Standard deviation, Coefficient of variation, Poincaré plots, maximum Floquet multipliers, short-term Lyapunov exponents, Recurrence quantification analysis, Multiscale entropy, Harmonic ratio and Index of harmonicity) were calculated on stride duration and trunk acceleration data (acquired at 100 Hz and 200 Hz) coming from straight walking windows and from windows including both straight walking and the directional change.

RESULTS

Harmonic ratio was the only measure that resulted to be affected by directional changes and sampling frequency, decreasing with the presence of a directional change task. HR was affected in the AP and V directions for the 200 Hz, but only in AP direction for the 100 Hz group.

CONCLUSION

Multiscale entropy, short term Lyapunov exponents and Recurrence quantification analysis were generally not affected by directional changes nor by sampling frequency, and could contribute to the definition of a fall risk index in free-walking conditions.

Gait variability and stability measures: minimum number of strides and within-session reliability

BACKGROUND

Several methods are proposed in the literature for the quantification of gait variability/stability from trunk accelerations. Since outputs can be influenced by implementation differences, reliability assessment and standardization of implementation parameters are still an issue. The aim of this study is to assess the minimum number of required strides and the within-session reliability of 11 variability/stability measures.

METHOD

Ten healthy participants walked in a straight line at self-selected speed wearing two synchronized tri-axial Inertial Measurement Units. Five variability measures were calculated based on stride times namely Standard deviation, Coefficient of variation, Inconsistency of variance, Nonstationary index and Poincaré plot. Six stability measures were calculated based on trunk accelerations namely Maximum Floquet multipliers, Short term/long term Lyapunov exponents, Recurrence quantification analysis, Multiscale entropy, Harmonic ratio and Index of harmonicity. The required minimum number of strides and the within-session reliability for each measure were obtained based on the interquartile range/mean ratio. Measures were classified in five categories (namely excellent, good, average, poor, and very poor) based on their reliability.

RESULTS

The number of strides required to obtain a reliable measure was generally larger than those conventionally used. Variability measures showed average to poor reliability, while stability measures ranged from excellent to very poor reliability.

CONCLUSION

Recurrence quantification analysis and multiscale entropy of trunk accelerations showed excellent reliability and a reasonable number of required strides. Based on these results, these measures should be taken into consideration in the assessment of fall risk.

Real-time human ambulation, activity, and physiological monitoring: taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions.