The ankle dorsiflexion kinetics demand to increase swing phase foot-ground clearance: implications for assistive device design and energy demands.

BACKGROUND: The ankle is usually highly effective in modulating the swing foot's trajectory to ensure safe ground clearance but there are few reports of ankle kinetics and mechanical energy exchange during the gait cycle swing phase. Previous work has investigated ankle swing mechanics during normal walking but with developments in devices providing dorsiflexion assistance, it is now essential to understand the minimal kinetic requirements for increasing ankle dorsiflexion, particularly for devices employing energy harvesting or utilizing lighter and lower power energy sources or actuators. METHODS: Using a real-time treadmill-walking biofeedback technique, swing phase ankle dorsiflexion was experimentally controlled to increase foot-ground clearance by 4 cm achieved via increased ankle dorsiflexion. Swing phase ankle moments and dorsiflexor muscle forces were estimated using AnyBody modeling system. It was hypothesized that increasing foot-ground clearance by 4 cm, employing only the ankle joint, would require significantly higher dorsiflexion moments and muscle forces than a normal walking control condition. RESULTS: Results did not confirm significantly increased ankle moments with augmented dorsiflexion, with 0.02 N.m/kg at toe-off reducing to zero by the end of swing. Tibialis Anterior muscle force incremented significantly from 2 to 4 N/kg after toe-off, due to coactivation with the Soleus. To ensure an additional 4 cm mid swing foot-ground clearance, an estimated additional 0.003 Joules/kg is required to be released immediately after toe-off. CONCLUSION: This study highlights the interplay between ankle moments, muscle forces, and energy demands during swing phase ankle dorsiflexion, offering insights for the design of ankle assistive technologies. External devices do not need to deliver significantly greater ankle moments to increase ankle dorsiflexion but, they should offer higher mechanical power to provide rapid bursts of energy to facilitate quick dorsiflexion transitions before reaching Minimum Foot Clearance event. Additionally, for ankle-related bio-inspired devices incorporating artificial muscles or humanoid robots that aim to replicate natural ankle biomechanics, the inclusion of supplementary Tibialis Anterior forces is crucial due to Tibialis Anterior and Soleus co-activation. These design strategies ensures that ankle assistive technologies are both effective and aligned with the biomechanical realities of human movement.

The highs and lows of lifting loads: SPM analysis of multi-segmental spine angles in healthy adults during manual handling with increased load.

Introduction: Manual handling personnel and those performing manual handling tasks in non-traditional manual handling industries continue to suffer debilitating and costly workplace injuries. Smart assistive devices are one solution to reducing musculoskeletal back injuries. Devices that provide targeted assistance need to be able to predict when and where to provide augmentation via predictive algorithms trained on functional datasets. The aim of this study was to describe how an increase in load impacts spine kinematics during a ground-to-platform manual handling task. Methods: Twenty-nine participants performed ground-to-platform lifts for six standardised loading conditions (50%, 60%, 70%, 80%, 90%, and 100% of maximum lift capacity). Six thoracic and lumbar spine segments were measured using inertial measurement units that were processed using an attitude-heading-reference filter and normalised to the duration of the lift. The lift was divided into four phases weight-acceptance, standing, lift-to-height and place-on-platform. Statistical significance of sagittal angles from the six spine segments were identified through statistical parametric mapping one-way analysis of variance with repeated measures and post hoc paired t-tests. Results: Two regions of interest were identified during a period of peak flexion and a period of peak extension. There was a significant increase in spine range of motion and peak extension angle for all spine segments when the load conditions were increased (p < 0.001). There was a decrease in spine angles (more flexion) during the weight acceptance to standing phase at the upper thoracic to upper lumbar spine segments for some condition comparisons. A significant increase in spine angles (more extension) during the place-on-platform phase was seen in all spine segments when comparing heavy loads (>80% maximum lift capacity, inclusive) to light loads (<80% maximum lift capacity) (p < 0.001). Discussion: The 50%-70% maximum lift capacity conditions being significantly different from heavier load conditions is representative that the kinematics of a lift do change consistently when a participant's load is increased. The understanding of how changes in loading are reflected in spine angles could inform the design of targeted assistance devices that can predict where and when in a task assistance may be needed, possibly reducing instances of back injuries in manual handling personnel.

Overground gait adaptability in older adults with type 2 diabetes in response to virtual targets and physical obstacles.

BACKGROUND: To step over an unexpected obstacle, individuals adapt gait; they adjust step length in the anterior-posterior direction prior to the obstacle and minimum toe clearance height in the vertical direction during obstacle avoidance. Inability to adapt gait may lead to falls in older adults with diabetes as the results of the effects of diabetes on the sensory-motor control system. Therefore, this study aimed to investigate gait adaptability in older adults with diabetes. RESEARCH QUESTION: Would diabetes impair gait adaptability and increase sagittal foot adjustment errors? METHODS: Three cohorts of 16 people were recruited: young adults (Group I), healthy older adults (Group II), and older adults with diabetes (Group III). Participants walked in baseline at their comfortable speeds. They then walked and responded to what was presented in gait adaptability tests, which included 40 trials with four random conditions: step shortening, step lengthening, obstacle avoiding, and walking through. Virtual step length targets were 40% of the baseline step length longer or shorter than the mean baseline step length; the actual obstacle was a 5-cm height across the walkway. A Vicon three-dimensional motion capture system and four A.M.T.I force plates were used to quantify spatiotemporal parameters of a gait cycle and sagittal foot adjustment errors (differences between desired and actual responses). Analyses of variance (ANOVA) repeated measured tests were used to investigate group and condition effects on dependent gait parameters at a significance level of 0.05. RESULTS: Statistical analyses of Group I (n = 16), Group II (n = 14) and Group III (n = 13) revealed that gait parameters did not differ between groups in baseline. However, they were significantly different in adaptability tests. Group III significantly increased their stance and double support times in adaptability tests, but these adaptations did not reduce their sagittal foot adjustment errors. They had the greatest step length errors and lowest toe-obstacle clearance, which could cause them to touch the obstacle more. SIGNIFICANCE: The presented gait adaptability tests may serve as entry tests for falls prevention programs.

Effects of diabetes mellitus on step length and minimum toe clearance adaptation.

BACKGROUND: Adaptive gait involves the ability to adjust the leading foot in response to the requirement of dynamic environments during walking. Accurate adjustments of the minimum toe clearance (MTC) height and step length can prevent older people from falling when walking and responding to hazards. Although older people with diabetes fall more frequently than healthy older adults, no previous studies have quantified their adaptive gait abilities. This study aimed to investigate the effects of diabetes mellitus on step length and MTC height adjustments using a non-immersive virtual-reality system. METHODS: Sixteen young adults (26 ± 5 years, 7 females), 16 healthy older adults (68 ± 5 years, 6 females), and 16 older adults with diabetes (70 ± 5 years, 6 females) completed adaptability tests while walking on a treadmill. A computer system visualised a continuous real-time signal of absolute step length and MTC on a monitor. Each person responded to four discrete participant-specific step length and MTC visual targets that were presented on the same signal. Tasks were to match the peaks of interest on each signal to presented targets. Targets were 10% longer or shorter than the mean baseline step length, and 2.5 cm, and 3.5 cm higher than the mean baseline MTC. When a target was displayed, it remained unchanged for 10 consecutive foot displacement adaptation attempts. Then, the target was removed and a new target or the same target was present after 10 consecutive steps and remained for 10 steps. Each target was randomly presented three times (3 × 10). Step length and MTC height adjustments in response to targets were measured and compared among groups. RESULTS: Mean preferred walking speeds were not different among groups significantly when no targets were presented on the monitor in baseline walking. However, when participants walked on a treadmill while attempting to match step lengths or MTC heights to displayed targets on the monitor, the group with diabetes had reduced step length and MTC adjustments compared with other groups significantly. They showed greater errors (differences between their step lengths/MTC heights and presented targets) on the monitor. CONCLUSIONS: This study quantified reduced abilities for older individuals with diabetes to adjust both step length and MTC in response to stimuli compared to healthy older counterparts. Reduced step length and MTC height adjustments can increase falls in at risk populations. The presented virtual-reality system has merits for assessing and training step and MTC adaptation.

State-of-the-Art Review on Wearable Obstacle Detection Systems Developed for Assistive Technologies and Footwear.

Walking independently is essential to maintaining our quality of life but safe locomotion depends on perceiving hazards in the everyday environment. To address this problem, there is an increasing focus on developing assistive technologies that can alert the user to the risk destabilizing foot contact with either the ground or obstacles, leading to a fall. Shoe-mounted sensor systems designed to monitor foot-obstacle interaction are being employed to identify tripping risk and provide corrective feedback. Advances in smart wearable technologies, integrating motion sensors with machine learning algorithms, has led to developments in shoe-mounted obstacle detection. The focus of this review is gait-assisting wearable sensors and hazard detection for pedestrians. This literature represents a research front that is critically important in paving the way towards practical, low-cost, wearable devices that can make walking safer and reduce the increasing financial and human costs of fall injuries.

The Influence of Cell Phone Usage on Dynamic Stability of the Body During Walking.

Dual-task walking and cell phone usage, which is associated with high cognitive load and reduced situational awareness, can increase risk of a collision, a fall event, or death. The objective of this study was to quantify the effect of dual-task cell phone talking, texting, and reading while walking on spatiotemporal gait parameters; minimum foot clearance; and dynamic stability of the lower limb joints, trunk, and head. Nineteen healthy male participants walked on an instrumented treadmill at their self-selected speed as well as walking while simultaneously (1) reading on a cell phone, (2) texting, and (3) talking on a cell phone. Gait analyses were performed using an optical motion analysis system, and dynamic stability was calculated using the Maximum Lyapunov Exponent. Dual-task cell phone usage had a significant destabilizing influence on the lower limb joints during walking. Cell phone talking while walking significantly increased step width and length and decreased minimum foot clearance height (P < .05). The findings suggest that dual-task walking and cell phone conversation may present a greater risk of a fall event than texting or reading. This may be due to the requirements for more rapid information processing and cognitive demand at the expense of motor control of joint stability.

Using Deep Learning to Predict Minimum Foot-Ground Clearance Event from Toe-Off Kinematics.

Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and the swing-phase event Minimum Foot Clearance (MFC) is recognised as the key biomechanical determinant of tripping probability. MFC is defined as the minimum swing foot clearance, which is seen approximately mid-swing, and it is routinely measured in gait biomechanics laboratories using precise, high-speed, camera-based 3D motion capture systems. For practical intervention strategies designed to predict, and possibly assist, swing foot trajectory to prevent tripping, identification of the MFC event is essential; however, no technique is currently available to determine MFC timing in real-life settings outside the laboratory. One strategy has been to use wearable sensors, such as Inertial Measurement Units (IMUs), but these data are limited to primarily providing only tri-axial linear acceleration and angular velocity. The aim of this study was to develop Machine Learning (ML) algorithms to predict MFC timing based on the preceding toe-off gait event. The ML algorithms were trained using 13 young adults' foot trajectory data recorded from an Optotrak 3D motion capture system. A Deep Learning configuration was developed based on a Recurrent Neural Network with a Long Short-Term Memory (LSTM) architecture and Huber loss-functions to minimise MFC-timing prediction error. We succeeded in predicting MFC timing from toe-off characteristics with a mean absolute error of 0.07 s. Although further algorithm training using population-specific inputs are needed. The ML algorithms designed here can be used for real-time actuation of wearable active devices to increase foot clearance at critical MFC and reduce devastating tripping falls. Further developments in ML-guided actuation for active exoskeletons could prove highly effective in developing technologies to reduce tripping-related falls across a range of gait impaired populations.

Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review.

Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human-machine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the user's locomotion intention and requirement, whereas machine-machine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environment-machine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided.

Biomechanical Correlates of Falls Risk in Gait Impaired Stroke Survivors.

Increased falls risk is prevalent among stroke survivors with gait impairments. Tripping is the leading cause of falls and it is highly associated with mid-swing Minimum Foot Clearance (MFC), when the foot's vertical margin from the walking surface is minimal. The current study investigated MFC characteristics of post-stroke individuals (n = 40) and healthy senior controls (n = 21) during preferred speed treadmill walking, using an Optotrak 3D motion capture system to record foot-ground clearance. In addition to MFC, bi-lateral spatio-temporal gait parameters, including step length, step width and double support time, were obtained for the post-stroke group's Unaffected and Affected limb and the control group's Dominant and Non-dominant limbs. Statistical analysis of MFC included central tendency (mean, median), step-to-step variability (standard deviation and interquartile range) and distribution (skewness and kurtosis). In addition, the first percentile, that is the lowest 1% of MFC values (MFC 1%) were computed to identify very high-risk foot trajectory control. Spatio-temporal parameters were described using the mean and standard deviation with a 2 × 2 (Group × Limb) Multivariate Analysis of Variance applied to determine significant Group and Limb effects. Pearson's correlations were used to reveal any interdependence between gait variables and MFC control. The main finding of the current research was that post-stroke group's affected limb demonstrated lower MFC 1% with higher variability and lower kurtosis. Post-stroke gait was also characterised by shorter step length, larger step width and increased double support time. Gait retraining methods, such as using real-time biofeedback, would, therefore, be recommended for post-stroke individuals, allowing them to acquire optimum swing foot control and reduce their tripping risk by elevating the swing foot and improving step-to-step consistency in gait control.

Exoskeleton Application to Military Manual Handling Tasks.

OBJECTIVE: The aim of this review was to determine how exoskeletons could assist Australian Defence Force personnel with manual handling tasks. BACKGROUND: Musculoskeletal injuries due to manual handling are physically damaging to personnel and financially costly to the Australian Defence Force. Exoskeletons may minimize injury risk by supporting, augmenting, and/or amplifying the user's physical abilities. Exoskeletons are therefore of interest in determining how they could support the unique needs of military manual handling personnel. METHOD: Industrial and military exoskeleton studies from 1990 to 2019 were identified in the literature. This included 67 unique exoskeletons, for which Information about their current state of development was tabulated. RESULTS: Exoskeleton support of manual handling tasks is largely through squat/deadlift (lower limb) systems (64%), with the proposed use case for these being load carrying (42%) and 78% of exoskeletons being active. Human-exoskeleton analysis was the most prevalent form of evaluation (68%) with reported reductions in back muscle activation of 15%-54%. CONCLUSION: The high frequency of citations of exoskeletons targeting load carrying reflects the need for devices that can support manual handling workers. Exoskeleton evaluation procedures varied across studies making comparisons difficult. The unique considerations for military applications, such as heavy external loads and load asymmetry, suggest that a significant adaptation to current technology or customized military-specific devices would be required for the introduction of exoskeletons into a military setting. APPLICATION: Exoskeletons in the literature and their potential to be adapted for application to military manual handling tasks are presented.

Measures of Bipedal Toe-Ground Clearance Asymmetry to Characterize Gait in Stroke Survivors.

Post-stroke hemiparesis often impairs gait and increases the risks of falls. Low and variable Minimum Toe Clearance (MTC) from the ground during the swing phase of the gait cycle has been identified as a major cause of such falls. In this paper, we study MTC characteristics in 30 chronic stroke patients, extracted from gait patterns during treadmill walking, using infrared sensors and motion analysis camera units. We propose objective measures to quantify MTC asymmetry between the paretic and non-paretic limbs using Poincaré analysis. We show that these subject independent Gait Asymmetry Indices (GAIs) represent temporal variations of relative MTC differences between the two limbs and can distinguish between healthy and stroke participants. Compared to traditional measures of cross-correlation between the MTC of the two limbs, these measures are better suited to automate gait monitoring during stroke rehabilitation. Further, we explore possible clusters within the stroke data by analysing temporal dispersion of MTC features, which reveals that the proposed GAIs can also be potentially used to quantify the severity of lower limb hemiparesis in chronic stroke.

State-of-the-Art Wearable Sensors and Possibilities for Radar in Fall Prevention.

Radar technology is constantly evolving, and new applications are arising, particularly for the millimeter wave bands. A novel application for radar is gait monitoring for fall prevention, which may play a key role in maintaining the quality of life of people as they age. Alarming statistics indicate that one in three adults aged 65 years or older will experience a fall every year. A review of the sensors used for gait analysis and their applications to technology-based fall prevention interventions was conducted, focusing on wearable devices and radar technology. Knowledge gaps were identified, such as wearable radar development, application specific signal processing and the use of machine learning algorithms for classification and risk assessment. Fall prevention through gait monitoring in the natural environment presents significant opportunities for further research. Wearable radar could be useful for measuring gait parameters and performing fall risk-assessment using statistical methods, and could also be used to monitor obstacles in real-time.

Prediction of gait trajectories based on the Long Short Term Memory neural networks.

The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82-5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.

A shoe-insole to improve ankle joint mechanics for injury prevention among older adults.

Technologies to assist senior individuals with active walking are important. This experiment aimed to investigate whether a customised insole geometry would reduce the risk of falls and locomotive injuries. The tested insole incorporated a built-in inclination to assist ankle dorsiflexion (2.2°) and eversion (4.5°). Twenty-six older adults and 30 younger counterparts undertook gait assessment with and without the experimental insole while 3 D motion capture and force plates recorded gait. The insole increased swing foot-ground clearance, with.43 cm for the older adults' dominant foot. The insole also prevented excessive lateral centre of pressure movement. The main insole effects on foot contact mechanics were (i) prolonged time to foot-flat (.015 s) and (ii) improved energy efficiency (2%). Reduced knee adduction moment (>15%) was observed in the older group. Shoe insoles to provide dorsiflexion and eversion support may have the potential to reduce the risk of falls and locomotion-related injuries for older adults.Practitioner Summary: Using 3 D gait assessment techniques this research investigated shoe-insoles incorporating ankle dorsiflexion and eversion support features. It was shown that falls risk and locomotive injuries could be reduced by the application of orthotics to support ankle dorsiflexion and eversion. Shoe-orthotics may provide practical low-cost solutions to correcting gait impairments.Abbreviations: MFC: minimum foot clearance; CoP: centre of pressure; OA: osteoarthritis; GRF: ground reaction forces; IREDS: infra-red light emitting diodes; PE: potential energy; KE: kinetic energy; IQR: interquartile range; ANOVA: analysis of variance.

A cross-sectional study of foot-ground clearance in healthy community dwelling Japanese cohorts aged 50, 60 and 70 years.

BACKGROUND: Falls-related injuries are particularly serious for older people, causing pain, reduced community engagement and associated medical costs. Tripping is the leading cause of falls and the current study examined whether minimum ground clearance (MFC) of the swing foot, indicating high tripping risk, would be differentiated across cohorts of healthy 50-, 60- and 70-years old community residents in Japan. METHODS: A cross-sectional population comprising the three groups (50s, 60s and 70s) of 123 Konosu City residents consented to be recorded when walking on an unobstructed surface at preferred speed. Gait biomechanics was measured using high speed (100 Hz) motion capture (OptiTrack - Natural Point Inc.), including step length and width, double support, foot contact angle and MFC (swing toe height above the ground). Multivariate Analysis of Variance (MANOVA) was used to confirm ageing effects on MFC and fundamental gait parameters. Pearson's correlations were performed to identify the relationships between mean MFC and other MFC characteristics (SD and SI), step length, step width, double support time and foot contact angle. RESULTS: Compared to 50s, lower step length was seen (2.69 cm and 6.15 cm) for 60s and 70s, respectively. No other statistical effects were identified for spatio-temporal parameters between the three groups. The 50s cohort MFC was also significantly higher than 60s and 70s, while step-to-step MFC variability was greater in the 70s than 50s and 60s. Pearson's correlations demonstrated that more symmetrical gait patterns were associated with greater MFC height, as reflected in greater symmetry in step width (50s), MFC (60s) and foot contact angle (70s). In the 70s increased MFC height correlated with higher MFC variability and reduced foot contact angle. CONCLUSIONS: MFC height reduces from 60 years but more variable MFC appears later, from 70 years. While symmetrical gait was accompanied by increased MFC height, in the 70s group attempts to increase MFC height may have caused more MFC variability and lower foot contact angles, compromising foot-ground clearance. Assessments of swing foot mechanics may be a useful component of community falls prevention.

Feasibility of Using Foot-Ground Clearance Biofeedback Training in Treadmill Walking for Post-Stroke Gait Rehabilitation.

Hemiplegic stroke often impairs gait and increases falls risk during rehabilitation. Tripping is the leading cause of falls, but the risk can be reduced by increasing vertical swing foot clearance, particularly at the mid-swing phase event, minimum foot clearance (MFC). Based on previous reports, real-time biofeedback training may increase MFC. Six post-stroke individuals undertook eight biofeedback training sessions over a month, in which an infrared marker attached to the front part of the shoe was tracked in real-time, showing vertical swing foot motion on a monitor installed in front of the subject during treadmill walking. A target increased MFC range was determined, and participants were instructed to control their MFC within the safe range. Gait assessment was conducted three times: Baseline, Post-training and one month from the final biofeedback training session. In addition to MFC, step length, step width, double support time and foot contact angle were measured. After biofeedback training, increased MFC with a trend of reduced step-to-step variability was observed. Correlation analysis revealed that MFC height of the unaffected limb had interlinks with step length and ankle angle. In contrast, for the affected limb, step width variability and MFC height were positively correlated. The current pilot-study suggested that biofeedback gait training may reduce tripping falls for post-stroke individuals.

Age effects on step adaptation during treadmill walking with continuous step length biofeedback.

BACKGROUND: The inability to adjust step length can lead to falls in older people when navigating everyday terrain. Precisely targeted forward placement of the leading foot, constituting step length adjustment, is required for adaptive gait control, but this ability may reduce with ageing. The objective of this study was to investigate ageing effects on step length adaptation using real-time biofeedback. RESEARCH QUESTION: Does ageing affect the ability to adapt step length to match a target using real-time biofeedback? METHODS: Fifteen older adults (67 ± 3 years; 8 females) and 27 young adults (24 ± 4 years; 13 females) completed a step length adaptation test while walking at preferred speed on a treadmill. The test involved walking while viewing a monitor at the front of the treadmill that showed a real-time signal of absolute left-right foot displacement. The task was to match the local maxima of the signal (i.e. step length) to two target conditions, at 10 % longer or 10 % shorter than mean baseline step length. When the target was displayed, it remained unchanged for a set of 10 consecutive step attempts. Three sets of 10 attempts for each target condition were allocated in random order, for a total of 30 step attempts per target. Average absolute error and average error (bias) of step length accuracy was computed for each target condition and compared between groups. RESULTS: The step adaptation test identified that older adults had greater mean absolute error for both short and long step targets and showed a step length-dependent bias significantly different to the young. SIGNIFICANCE: Real-time foot position feedback could be a useful tool to train and evaluate step adaptation in older people.

Lower Limb Kinematics Trajectory Prediction Using Long Short-Term Memory Neural Networks.

This study determined whether the kinematics of lower limb trajectories during walking could be extrapolated using long short-term memory (LSTM) neural networks. It was hypothesised that LSTM auto encoders could reliably forecast multiple time-step trajectories of the lower limb kinematics, specifically linear acceleration (LA) and angular velocity (AV). Using 3D motion capture, lower limb position-time coordinates were sampled (100 Hz) from six male participants (age 22 ± 2 years, height 1.77 ± 0.02 m, body mass 82 ± 4 kg) who walked for 10 min at 5 km/h on a 0% gradient motor-driven treadmill. These data were fed into an LSTM model with a sliding window of four kinematic variables with 25 samples or time steps: LA and AV for thigh and shank. The LSTM was tested to forecast five samples (i.e., time steps) of the four kinematic input variables. To attain generalisation, the model was trained on a dataset of 2,665 strides from five participants and evaluated on a test set of 1 stride from a sixth participant. The LSTM model learned the lower limb kinematic trajectories using the training samples and tested for generalisation across participants. The forecasting horizon suggested higher model reliability in predicting earlier future trajectories. The mean absolute error (MAE) was evaluated on each variable across the single tested stride, and for the five-sample forecast, it obtained 0.047 m/s2 thigh LA, 0.047 m/s2 shank LA, 0.028 deg/s thigh AV and 0.024 deg/s shank AV. All predicted trajectories were highly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The motion prediction model may have a wide range of applications, such as mitigating the risk of falls or balance loss and improving the human-machine interface for wearable assistive devices.

Effects of Visually Augmented Gait Training on Foot-Ground Clearance: An Intervention to Reduce Tripping-Related Falls.

Minimum toe clearance (MTC ∼10-30 mm) is a hazardous mid-swing gait event, characterized by high-foot velocity (∼4.60 m·s-1) and single-foot support. This experiment tested treadmill-based gait training effects on MTC. Participants were 10 young (4 males and 6 females) and 10 older (6 males and 4 females) healthy ambulant individuals. The mean age, stature, and body mass for the younger group was 23 (2) years, 1.72 (0.10) m, and 67.5 (8.3) kg, and for older adults was 77 (9) years, 1.64 (0.10) m, and 71.1 (12.2) kg. Ten minutes of preferred speed treadmill walking (baseline) was followed by 20 minutes with MTC information (feedback) and 10 minutes without feedback (retention). There were no aging effects on MTC in baseline or feedback. The MTC in baseline for older adults was 14.2 (3.5) mm and feedback 27.5 (8.7) mm, and for the younger group, baseline was 12.7 (2.6) mm and feedback 28.8 (5.1) mm, respectively. Retention MTC was significantly higher for both groups, indicating a positive effect of augmented information: younger 40.8 (7.3) mm and older 27.7 (13.6) mm. Retention joint angles relative to baseline indicated that the young modulated joint angles control MTC differently using increased ankle dorsiflexion at toe off and modulating knee and hip angles later in swing closer to MTC.