Gait Events Research Articles

Comparison of Three Motion Capture-Based Algorithms for Spatiotemporal Gait Characteristics: How Do Algorithms Affect Accuracy and Precision of Clinical Outcomes?

Gait assessment is of interest to clinicians and researchers because it provides information about patients' functional mobility. Optoelectronic camera-based systems with gait event detection algorithms are considered the gold standard for gait assessment. Yet, the choice of the algorithm used to process data and extract the desired parameters from those detected gait events has an impact on the validity and reliability of the gait parameters computed. There are multiple techniques documented in the literature for computing gait events, including the analysis of the minimal position of the heel and toe markers, the computation of the relative distance between sacrum and foot markers, and the assessment of the smallest distance between the heel and toe markers. Validation studies conducted on these algorithms report variations in accuracy. Yet, these studies were conducted in different conditions, at varying gait velocities, and on different populations. The purpose of this study is to compare accuracy, precision, and robustness of three algorithms using motion capture data obtained from 25 healthy persons and 21 psoriatic arthritic patients walking at three distinct speeds on an instrumented treadmill. Errors in gait events recognition (heel strike-HS and toe-off-TO) and their impact on gait metrics (stance phase and stride length) are reported and compared to ground reaction force events measured with force plates. Over the 9114 collected steps across all walking speeds, more than 99% of gait events were recognized by all algorithms. On average, HS events were detected within 1.2 ms of the reference for two algorithms, while the third one detected HS late, with an average detection error of 40.7 ms. Yet, significant variations in accuracy were noted with gait speed; the performance decreased for all algorithms at slow speed. TO events were identified early by all algorithms, with an average error ranging from 16.0 to 100.0 ms. These gait events errors lead to 2-15% inaccuracies in stance phase assessment, while the impact on stride length remains below 0.3 cm. Overall, the algorithm based on the relative distance between the sacral and foot markers stood out for its accuracy, precision, and robustness at all walking speeds.

Estimation of ground reaction force waveforms during fixed pace running outside the laboratory.

In laboratory experiments, biomechanical data collections with wearable technologies and machine learning have been promising. Despite the development of lightweight portable sensors and algorithms for the identification of gait events and estimation of kinetic waveforms, machine learning models have yet to be used to full potential. We propose the use of a Long Short Term Memory network to map inertial data to ground reaction force data gathered in a semi-uncontrolled environment. Fifteen healthy runners were recruited for this study, with varied running experience: novice to highly trained runners (<15 min 5 km race), and ages ranging from 18 to 64 years old. Force sensing insoles were used to measure normal foot-shoe forces, providing the standard for identification of gait events and measurement of kinetic waveforms. Three inertial measurement units (IMUs) were mounted to each participant, two bilaterally on the dorsal aspect of the foot and one clipped to the back of each participant's waistband, approximating their sacrum. Data input into the Long Short Term Memory network were from the three IMUs and output were estimated kinetic waveforms, compared against the standard of the force sensing insoles. The range of RMSE for each stance phase was from 0.189-0.288 BW, which is similar to multiple previous studies. Estimation of foot contact had an r 2 = 0.795. Estimation of kinetic variables varied, with peak force presenting the best output with an r 2 = 0.614. In conclusion, we have shown that at controlled paces over level ground a Long Short Term Memory network can estimate 4 s temporal windows of ground reaction force data across a range of running speeds.

Inertial Sensor Location for Ground Reaction Force and Gait Event Detection Using Reservoir Computing in Gait.

Inertial measurement units (IMUs) have shown promising outcomes for estimating gait event detection (GED) and ground reaction force (GRF). This study aims to determine the best sensor location for GED and GRF prediction in gait using data from IMUs for healthy and medial knee osteoarthritis (MKOA) individuals. In this study, 27 healthy and 18 MKOA individuals participated. Participants walked at different speeds on an instrumented treadmill. Five synchronized IMUs (Physilog®, 200 Hz) were placed on the lower limb (top of the shoe, heel, above medial malleolus, middle and front of tibia, and on medial of shank close to knee joint). To predict GRF and GED, an artificial neural network known as reservoir computing was trained using combinations of acceleration signals retrieved from each IMU. For GRF prediction, the best sensor location was top of the shoe for 72.2% and 41.7% of individuals in the healthy and MKOA populations, respectively, based on the minimum value of the mean absolute error (MAE). For GED, the minimum MAE value for both groups was for middle and front of tibia, then top of the shoe. This study demonstrates that top of the shoe is the best sensor location for GED and GRF prediction.

Estimation of gait events and kinetic waveforms with wearable sensors and machine learning when running in an unconstrained environment

Wearable sensors and machine learning algorithms are becoming a viable alternative for biomechanical analysis outside of the laboratory. The purpose of this work was to estimate gait events from inertial measurement units (IMUs) and utilize machine learning for the estimation of ground reaction force (GRF) waveforms. Sixteen healthy runners were recruited for this study, with varied running experience. Force sensing insoles were used to measure normal foot-shoe forces, providing a proxy for vertical GRF and a standard for the identification of gait events. Three IMUs were mounted on each participant, two bilaterally on the dorsal aspect of each foot and one clipped to the back of each participant’s waistband, approximating their sacrum. Participants also wore a GPS watch to record elevation and velocity. A Bidirectional Long Short Term Memory Network (BD-LSTM) was used to estimate GRF waveforms from inertial waveforms. Gait event estimation from both IMU data and machine learning algorithms led to accurate estimations of contact time. The GRF magnitudes were generally underestimated by the machine learning algorithm when presented with data from a novel participant, especially at faster running speeds. This work demonstrated that estimation of GRF waveforms is feasible across a range of running velocities and at different grades in an uncontrolled environment.

Gait Event Prediction Using Surface Electromyography in Parkinsonian Patients

Gait disturbances are common manifestations of Parkinson's disease (PD), with unmet therapeutic needs. Inertial measurement units (IMUs) are capable of monitoring gait, but they lack neurophysiological information that may be crucial for studying gait disturbances in these patients. Here, we present a machine learning approach to approximate IMU angular velocity profiles and subsequently gait events using electromyographic (EMG) channels during overground walking in patients with PD. We recorded six parkinsonian patients while they walked for at least three minutes. Patient-agnostic regression models were trained on temporally embedded EMG time series of different combinations of up to five leg muscles bilaterally (i.e., tibialis anterior, soleus, gastrocnemius medialis, gastrocnemius lateralis, and vastus lateralis). Gait events could be detected with high temporal precision (median displacement of <50 ms), low numbers of missed events (<2%), and next to no false-positive event detections (<0.1%). Swing and stance phases could thus be determined with high fidelity (median F1-score of ~0.9). Interestingly, the best performance was obtained using as few as two EMG probes placed on the left and right vastus lateralis. Our results demonstrate the practical utility of the proposed EMG-based system for gait event prediction, which allows the simultaneous acquisition of an electromyographic signal to be performed. This gait analysis approach has the potential to make additional measurement devices such as IMUs and force plates less essential, thereby reducing financial and preparation overheads and discomfort factors in gait studies.

A Domain Adaptive Convolutional Neural Network for sEMG-Based Gait Phase Recognition Against to Speed Changes

Gait phase recognition based on surface electromyography (sEMG) signals provides a human–robot interface (HRI) for rehabilitation robots. However, a high-performance deep learning model requires to be calibrated on a large amount of data and frequently recalibrated for new gait speeds, which raises a burden on users. In this article, a domain adaptive convolutional neural network (DACNN) model for sEMG-based gait phase recognition against speed changes is proposed, which only needs to be pretrained on a comfortable gait speed (C.G.) and quick recalibrated on new gait speeds. Specifically, a CNN-based backbone model (BM) is first constructed and pretrained using C.G. data. Then, a domain adaptation (DA) algorithm is applied for recalibration, which allows the BM to learn speed-invariant representation with a small amount of new speed data. Four subjects participated in the experiment and were required to walk at four gait speeds, and three BMs based on AlexNet, LeNet, and fusion-data-CNN (FDCNN) were set as architectural options. Compared with the fine-tuning (FT) and non-recalibration (NoRC) strategies, the results show that the proposed method significantly outperforms both baseline methods. By recalibrating on 6-s data, the proposed method can achieve an accuracy improvement of 1.5%–6.1% compared with FT and 58%–81% compared with NoRC. The average gait phase recognition accuracies are 58.13% for AlexNet, 81.56% for LeNet, and 81.53% for FDCNN, respectively. The average mean absolute errors for gait event identification are 48, 85, and 66 ms, respectively. These results indicate that the DACNN with low recalibration burden can improve the usability of sEMG-based pattern recognition systems.

Impact of Gait Events Identification through Wearable Inertial Sensors on Clinical Gait Analysis of Children with Idiopathic Toe Walking

Idiopathic toe walking (ITW) is a gait deviation characterized by forefoot contact with the ground and excessive ankle plantarflexion over the entire gait cycle observed in otherwise-typical developing children. The clinical evaluation of ITW is usually performed using optoelectronic systems analyzing the sagittal component of ankle kinematics and kinetics. However, in standardized laboratory contexts, these children can adopt a typical walking pattern instead of a toe walk, thus hindering the laboratory-based clinical evaluation. With these premises, measuring gait in a more ecological environment may be crucial in this population. As a first step towards adopting wearable clinical protocols embedding magneto-inertial sensors and pressure insoles, this study analyzed the performance of three algorithms for gait events identification based on shank and/or foot sensors. Foot strike and foot off were estimated from gait measurements taken from children with ITW walking barefoot and while wearing a foot orthosis. Although no single algorithm stands out as best from all perspectives, preferable algorithms were devised for event identification, temporal parameters estimate and heel and forefoot rocker identification, depending on the barefoot/shoed condition. Errors more often led to an erroneous characterization of the heel rocker, especially in shoed condition. The ITW gait specificity may cause errors in the identification of the foot strike which, in turn, influences the characterization of the heel rocker and, therefore, of the pathologic ITW behavior.

Human Gait Activity Recognition Machine Learning Methods.

Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject's quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm's robustness was also verified with the successful detection of freezing gait episodes in a Parkinson's disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.

Analysis of the Recent AI for Pedestrian Navigation With Wearable Inertial Sensors

Wearable devices embedding inertial sensors enable autonomous, seamless, and low-cost pedestrian navigation. Appealing it is, the approach faces several challenges: measurement noises, different device-carrying modes, different user dynamics, and individual walking characteristics. Recent research applies Artificial Intelligence (AI) to improve inertial navigation's robustness and accuracy. Our analysis identifies 2 main categories of AI approaches depending on the inertial signals segmentation: either using human gait events (steps or strides) or fixed-length inertial data segments. A theoretical analysis of the fundamental assumptions is carried out for each category. Two state-of-the-art AI algorithms (SELDA, RoNIN), representative of each category, and a gait-driven non-AI method (SmartWalk) are evaluated in a 2,17 km long open access dataset, representative of the diversity of pedestrians' mobility surroundings (open-sky, indoors, forest, urban, parking lot). SELDA is an AI-based stride length estimation algorithm, RoNIN is an AI-based positioning method, and SmartWalk is a gait-driven non-AI positioning method. The experimental assessment shows the distinct features in each category and their limits with respect to the underlying hypotheses. On average, SELDA, RoNIN, and SmartWalk achieve 8 m, 22 m, and 17 m average positioning errors (RMSE) respectively, on six testing tracks recorded with two volunteers in various environments.

Human gait-labeling uncertainty and a hybrid model for gait segmentation.

Evaluate manual labeling uncertainty and introduce a hybrid stride detection and gait-event estimation model for autonomous, long-term, and remote monitoring. Estimate inter-labeler inconsistencies by computing the limits-of-agreement. Develop a hybrid model based on dynamic time warping and convolutional neural network to identify valid strides and eliminate non-stride data in inertial (walking) data collected by a wearable device. Finally, detect gait events within a valid stride region. The limits of inter-labeler agreement for key gait events heel off, toe off, heel strike, and flat foot are 72, 16, 24, and 80 ms, respectively; The hybrid model's classification accuracy for stride and non-stride are 95.16 and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24, 5, 9, and 13 ms, respectively, when compared to the average human labels. The results show the inherent labeling uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers, and it is a valid model to reliably detect strides and estimate the gait events in human gait data. This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.

Rules-Based Real-Time Gait Event Detection Algorithm for Lower-Limb Prosthesis Users during Level-Ground and Ramp Walking.

Real-time gait event detection (GED) using inertial sensors is important for applications such as remote gait assessments, intelligent assistive devices including microprocessor-based prostheses or exoskeletons, and gait training systems. GED algorithms using acceleration and/or angular velocity signals achieve reasonable performance; however, most are not suited for real-time applications involving clinical populations walking in free-living environments. The aim of this study was to develop and evaluate a real-time rules-based GED algorithm with low latency and high accuracy and sensitivity across different walking states and participant groups. The algorithm was evaluated using gait data collected from seven able-bodied (AB) and seven lower-limb prosthesis user (LLPU) participants for three walking states (level-ground walking (LGW), ramp ascent (RA), ramp descent (RD)). The performance (sensitivity and temporal error) was compared to a validated motion capture system. The overall sensitivity was 98.87% for AB and 97.05% and 93.51% for LLPU intact and prosthetic sides, respectively, across all walking states (LGW, RA, RD). The overall temporal error (in milliseconds) for both FS and FO was 10 (0, 20) for AB and 10 (0, 25) and 10 (0, 20) for the LLPU intact and prosthetic sides, respectively, across all walking states. Finally, the overall error (as a percentage of gait cycle) was 0.96 (0, 1.92) for AB and 0.83 (0, 2.08) and 0.83 (0, 1.66) for the LLPU intact and prosthetic sides, respectively, across all walking states. Compared to other studies and algorithms, the herein-developed algorithm concurrently achieves high sensitivity and low temporal error with near real-time detection of gait in both typical and clinical populations walking over a variety of terrains.

Gait Events Prediction Using Hybrid CNN-RNN-Based Deep Learning Models through a Single Waist-Worn Wearable Sensor.

Elderly gait is a source of rich information about their physical and mental health condition. As an alternative to the multiple sensors on the lower body parts, a single sensor on the pelvis has a positional advantage and an abundance of information acquirable. This study aimed to improve the accuracy of gait event detection in the elderly using a single sensor on the waist and deep learning models. Data were gathered from elderly subjects equipped with three IMU sensors while they walked. The input taken only from the waist sensor was used to train 16 deep-learning models including a CNN, RNN, and CNN-RNN hybrid with or without the Bidirectional and Attention mechanism. The groundtruth was extracted from foot IMU sensors. A fairly high accuracy of 99.73% and 93.89% was achieved by the CNN-BiGRU-Att model at the tolerance window of ±6 TS (±6 ms) and ±1 TS (±1 ms), respectively. Advancing from the previous studies exploring gait event detection, the model demonstrated a great improvement in terms of its prediction error having an MAE of 6.239 ms and 5.24 ms for HS and TO events, respectively, at the tolerance window of ±1 TS. The results demonstrated that the use of CNN-RNN hybrid models with Attention and Bidirectional mechanisms is promising for accurate gait event detection using a single waist sensor. The study can contribute to reducing the burden of gait detection and increase its applicability in future wearable devices that can be used for remote health monitoring (RHM) or diagnosis based thereon.

Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection.

Accelerometers are low-cost measurement devices that can readily be used outside the lab. However, determining isolated gait events from accelerometer signals, especially foot-off events during running, is an open problem. We outline a two-step approach where machine learning serves to predict vertical ground reaction forces from accelerometer signals, followed by force-based event detection. We collected shank accelerometer signals and ground reaction forces from 21 adults during comfortable walking and running on an instrumented treadmill. We trained one common reservoir computer using segmented data using both walking and running data. Despite being trained on just a small number of strides, this reservoir computer predicted vertical ground reaction forces in continuous gait with high quality. The subsequent foot contact and foot off event detection proved highly accurate when compared to the gold standard based on co-registered ground reaction forces. Our proof-of-concept illustrates the capacity of combining accelerometry with machine learning for detecting isolated gait events irrespective of mode of locomotion.

A kinematic-geometric model based on ankles’ depth trajectory in frontal plane for gait analysis using a single RGB-D camera

The emergence of RGB-D cameras and the development of pose estimation algorithms offer opportunities in biomechanics. However, some challenges still remain when using them for gait analysis, including noise which leads to misidentification of gait events and inaccuracy.Therefore, we present a novel kinematic-geometric model for spatio-temporal gait analysis, based on ankles’ trajectory in the frontal plane and distance-to-camera data (depth). Our approach consists of three main steps: identification of the gait pattern and modeling via parameterized curves, development of a fitting algorithm, and computation of locomotive indices. The proposed fitting algorithm applies on both ankles’ depth data simultaneously, by minimizing through numerical optimization some geometric and biomechanical error functions.For validation, 15 subjects were asked to walk inside the walkway of the OptoGait, while the OptoGait and an RGB-D camera (Microsoft Azure Kinect) were both recording. Then, the spatio-temporal parameters of both feet were computed using the OptoGait and the proposed model. Validation results show that the proposed model yields good to excellent absolute statistical agreement (0.86 ≤ Rc ≤ 0.99).Our kinematic-geometric model offers several benefits: (1) It relies only on the ankles’ depth trajectory both for gait events extraction and spatio-temporal parameters’ calculation; (2) it is usable with any kind of RGB-D camera or even with 3D marker-based motion analysis systems in absence of toes’ and heels’ markers; and (3) it enables improving the results by denoising and smoothing the ankles’ depth trajectory. Hence, the proposed kinematic-geometric model facilitates the development of portable markerless systems for accurate gait analysis.

Estimation of Gait Parameters for Transfemoral Amputees Using Lower Limb Kinematics and Deterministic Algorithms.

Accurate estimation of gait parameters depends on the prediction of key gait events of heel strike (HS) and toe-off (TO). Kinematics-based gait event estimation has shown potential in this regard, particularly using leg and foot velocity signals and gyroscopic sensors. However, existing algorithms demonstrate a varying degree of accuracy for different populations. Moreover, the literature lacks evidence for their validity for the amputee population. The purpose of this study is to evaluate this paradigm to predict TO and HS instants and to propose a new algorithm for gait parameter estimation for the amputee population. An open data set containing marker data of 12 subjects with unilateral transfemoral amputation during treadmill walking was used, containing around 3400 gait cycles. Five deterministic algorithms detecting the landmarks (maxima, minima, and zero-crossings [ZC]) in the foot, shank, and thigh angular velocity data indicating HS and TO events were implemented and their results compared against the reference data. Two algorithms based on foot and shank velocity minima performed exceptionally well for the HS prediction, with median accuracy in the range of 6–13 ms. However, both these algorithms produced inferior accuracy for the TO event with consistent early prediction. The peak in the thigh velocity produced the best result for the TO prediction with <25 ms median error. By combining the HS prediction using shank velocity and TO prediction from the thigh velocity, the algorithm produced the best results for temporal gait parameters (step, stride times, stance, and double support timings) with a median error of less than 25 ms. In conclusion, combined shank and thigh velocity-based prediction leads to improved gait parameter estimation than traditional algorithms for the amputee population.

A deep-learning approach for automatically detecting gait-events based on foot-marker kinematics in children with cerebral palsy-Which markers work best for which gait patterns?

Neuromotor pathologies often cause motor deficits and deviations from typical locomotion, reducing the quality of life. Clinical gait analysis is used to effectively classify these motor deficits to gain deeper insights into resulting walking behaviours. To allow the ensemble averaging of spatio-temporal metrics across individuals during walking, gait events, such as initial contact (IC) or toe-off (TO), are extracted through either manual annotation based on video data, or through force thresholds using force plates. This study developed a deep-learning long short-term memory (LSTM) approach to detect IC and TO automatically based on foot-marker kinematics of 363 cerebral palsy subjects (age: 11.8 ± 3.2). These foot-marker kinematics, including 3D positions and velocities of the markers located on the hallux (HLX), calcaneus (HEE), distal second metatarsal (TOE), and proximal fifth metatarsal (PMT5), were extracted retrospectively from standard barefoot gait analysis sessions. Different input combinations of these four foot-markers were evaluated across three gait subgroups (IC with the heel, midfoot, or forefoot). For the overall group, our approach detected 89.7% of ICs within 16ms of the true event with a 18.5% false alarm rate. For TOs, only 71.6% of events were detected with a 33.8% false alarm rate. While the TOE|HEE marker combination performed well across all subgroups for IC detection, optimal performance for TO detection required different input markers per subgroup with performance differences of 5-10%. Thus, deep-learning LSTM based detection of IC events using the TOE|HEE markers offers an automated alternative to avoid operator-dependent and laborious manual annotation, as well as the limited step coverage and inability to measure assisted walking for force plate-based detection of IC events.

Inertial measurement units-based assessment of gait to identify depression

Major Depressive Disorder (MDD) causes a variety of emotional and physical issues leading to severe clinical implications [1]. In MDD patients, a strong association between psychomotor deterioration and gait components has been found [2]. Instrumental gait analysis holds a crucial role in precision evaluation of movement in MDD [3]. Targeting a solution for an ecological assessment of movement, Inertial Measurement Units (IMUs) signals were recorded during a Timed Up and Go (TUG) test to investigate temporal and spatial parameters in both MDD patients and age matched controls. 48 participants, (28 Controls, C and 14 Depressed, D), were enrolled into the study after signing a written consent. Four IMUs (Shimmer Sensing), placed at both ankles, at the trunk and at the pelvis, were adopted with a sampling frequency of 250 Hz while subjects performed 3 trials of a 7m-TUG test. After filtering (4 Hz low pass filter) and segmenting IMU raw data in the 3 main TUG test phases (standing, walking and sitting), gait events (Toe Off, Heel Strike and Mid Swing) were detected from signal coming from ankle-worn IMUs to drive gait spatio-temporal parameters estimation (Fig. 1). In addition, using pelvis and trunk sensors, acceleration ranges and the gyroscope complexity index (CI) [4] were calculated along the 3 directions (antero-posterior AP, cranio-caudal CC and medio- lateral ML). The median among the 3 trials was retained to evaluate the correlation between instrumental parameters and the presence (via a logistic regression adjusted for age and sex). Significance threshold was set at p-value=0.05. The C group included 8 males and 20 females (median age 23 years [IQR=3]) and the D group included 2 males and 12 females (median age 22 years [IQR=5]). Only ML acceleration range for pelvis and AP gyroscope CI for trunk were significantly associated to depression. The identification of depression markers from wearable sensors could enable the long-term objective of depression mass screening using portable devices ( Fig. 1 ). Among the tested parameters, a larger pelvis ML acceleration range was found in patients, coherently with previous literature findings [1]. As Adolph et al. [5] noticed such higher oscillation can be associated to the request of walking on a pre-defined path, imposing an external constraint on the walk, thus making this parameter an object for further investigations in ecological setting. Differently from previous studies analyzing longer walking bouts [6], we were unable to identify a significant reduction of walking speed in the D group. Up to our knowledge, no study compared CI between healthy individuals and depressed patients, preventing the comparison of our results to literature findings. Nevertheless, the AP gyroscope CI, that contains meaningful information on the posture stability, is proved to be a predictor of the MDD. Overall, such parameters may be used in a Machine Learning model embedded in portable devices to predict the occurrence of MDD.

Estimation of running biomechanical parameters using magneto-inertial sensors: a preliminary investigation

Many runners perform training sessions outdoors and on varying surfaces (e.g., track, paved path, trail). Training surface has been shown to influence running spatiotemporal measures and stiffness [1]. However, applied research in understanding training surface influence has been limited by lack of robust methodology to capture biomechanics in-the-field. The increasing availability of wearable magneto- inertial sensors (MIMUs) is now making it possible to perform in-field assessment of running biomechanics [2]. Validation of new inertial-based methods is vital to widespread field use. In this study, we compared inertial-based methods for running biomechanical analysis and against gold-standard stereo-photogrammetric system (SP). Ten recreational male runners (age: 32.3 ± 10.0 years) were enrolled and asked to run on a treadmill at 14 km/hr for 8 trials lasting 6 minutes. We simulated changes in surface in a controlled lab setting by using 8 different footwear conditions. Participants were instrumented with MIMUs (mod: Opal v2, APDM, Portland, USA; fs=200 sample/s) attached on the pelvis (L5) and the instep of both feet, and retroreflective markers on heels, toes, and each MIMU (recorded with a 9-camera Vero system, Vicon, Oxford, UK; fs=200 sample/s). The estimated relevant biomechanical parameters included: stride/stance/swing duration, stride rate, vertical oscillations of the center of mass (CoM), and vertical/leg stiffness. Gait events (initial and final contacts) were estimated using a modified version of the MIMU-based method proposed by Benson et al. [2]. CoM vertical displacement was estimated as the vertical component of the L5 MIMU trajectory. Vertical and leg stiffness were computed starting from CoM displacement, stance interval, and swing interval [3]. For each participant and trial condition, errors with respect to SP were computed (80 values for each parameter) and root mean squared errors (RMSE) across participants and conditions were then calculated. In addition, a Bland-Altman analysis was used to quantify the agreement between MIMU and SP outcomes in terms of 50% limits of agreement interval (LoAI). Differences between MIMU-based and SP-based parameters over all the runners are reported in Table 1. Results showed high accuracy especially in the estimation of stride duration, stride rate and CoM vertical displacement. The level of uncertainty associated to each biomechanical variable, as provided by the LoAIs, can be used to detect differences in surface conditions (footwear conditions). Future analysis and development of field methods will investigate the influence of unconstrained movements (outdoor, no treadmill) in addition to varying surface conditions and the influence of running speed on inertial-based method accuracy.

Smart Lacelock: A Novel Shoelace Tensioning Device for Human Motion Sensing

Shoe-mounted wearable sensors may serve a variety of important purposes (activity recognition, energy expenditure estimation, and so on) by providing rich information about human locomotion. However, reliably attaching such sensors to shoes still remains a challenge. Furthermore, pressure-sensing elements in such shoe sensor systems often suffer from poor durability due to the large dynamic load. This work presents a novel shoe-mounted sensor named Smart Lacelock, which takes the form of a common shoelace tensioning device (shoelace lock). With its unique form factor, the Smart Lacelock can be securely attached to the top of a shoe with minimal effort, enabling its embedded inertia measurement unit (IMU) to provide reliable 3-D motion measurement of the foot. Furthermore, the Smart Lacelock incorporates a load cell to measure the force applied by the shoelace, providing valuable information related to ankle movement, foot/shoe shape change, and ground force. Design details of the device are presented, including the mechanical structure and electronic circuitry. We also conducted a ten-participant human study, in which signals were recorded during free ankle swing, body weight (BW) shifting, sit-to-stand (STS), and overground walking. The results demonstrated the Smart Lacelock’s capability of providing consistent and observable responses to single contributing factors (ankle movement and foot loading) as well as complex movements with clearly defined events of interest (STS and walking). Such unique capability suggests that the Smart Lacelock may serve as an important source of human movement information to support related applications such as activity recognition and gait event detection.

E-Textile smart socks for gait analysis: a preliminary validation study

In the last years wearable technologies and smart fabrics are arousing a great deal of interest in the healthcare field. The possibility to directly interact with the body, along with the comfort of fabrics, represent great opportunities to develop interesting applications. Smart sensing socks, able to collect kinematic and/or foot pressure signals, are attractive solutions to perform gait analysis also outside clinical laboratories, e.g., in the home environment for remote continuous monitoring of patients. In this study we analyse the performance of Sensoria smart socks (Sensoria Health Inc. Seattle, WA, USA) in detecting the major spatio-temporal gait analysis metrics. We validate the results provided by the system in comparison with those of the IMU-based gait analysis system OPAL Mobility Lab by APDM (APDM Inc, Portland, OR, USA). Three textile pressure sensors are embedded in the Sensoria Sock to monitor plantar pressure, while the hardware unit is connected on the lateral part of the ankle to measure kinematic signals and transmit data to a smartphone. Twelve records were acquired on five healthy subjects (2 women). Each subject wore the sensing Sensoria smart socks and three OPAL Inertial Measurement Units (two on the feet and one in lumbar position), in order to perform simultaneous recording of the walking. The trial consisted of walking 10 metres, turning and returning to the starting point at preferred speed. The following resulting gait metrics were considered from the two systems and compared: Gait Cycle Time (GCT) [s], Stance Phase [%GCT], Cadence [steps/min]. Statistical comparison was performed by means of Bland-Altman test. Fig. 1 shows the results of the statistical analysis. Fig. 1 a presents the principal statistics of the considered metrics for the two systems. The table in Fig. 1 a also reports the numeric results of Bland- Altman test, expressed as the average bias (Sensoria minus OPAL) and its 95% Confidence Interval (CI) (Lower Bound LB – Upper Bound UB). Fig. 1 b, c and d respectively represent the Bland- Altman Plots for GCT, Stance Phase and Cadence. In this preliminary study we investigate the performances of the wearable Sensoria smart socks in evaluating the major temporal gait metrics. Results underline a general agreement in measuring GCT ( bias = − 0.00593 s ) and Cadence ( bias = − 0.0820 steps / min ). The confidence interval of bias includes the zero for both metrics, indicating that the differences randomly occur and are not systematic. The Bland-Altman plot for Stance Phase metric shows a significant difference between the measures provided by the two systems: Sensoria smart socks underestimate the foot stance phase duration with respect to the reference system OPAL Mobility Lab. In this case the average bias is − 7.75 % and its confidence interval does not include zero. The agreement in GCT values and the simultaneous disagreement of stance phase demonstrate discrepancies between the two systems in the detection of intermediate gait events (initial and terminal contact). In conclusion, smart socks performance in the detection of GCT and cadence are satisfactory, however improvements are needed in order to assess more specific gait metrics. In future studies, we aim to test the socks on a larger population and in the assessment of other temporal and spatial gait metrics.