Gait Feature Extraction Research Articles

Preference-Based Assistance Map Learning With Robust Adaptive Oscillators

Recently, lower-limb exoskeletons have demonstrated the ability to enhance human mobility by reducing biological efforts with human-in-the-loop (HIL) optimization. However, this technology is confined to the laboratory, and it is difficult to generalize to daily applications where gaits are more complex and professional equipment is not accessible. To solve this issue, firstly, we present a robust adaptive oscillator (RAO) to synchronize the human-robot movement and extract gait features. Then, we use the Gaussian process regression (GPR) to map the subjects’ preferred assistance parameters to gait features. Experiments show that the RAO has a faster convergence rate compared with the traditional adaptive oscillators. Meanwhile, the learning efficiency of the proposed method shows superiority compared with the HIL optimization. The effectiveness of the proposed method is validated by a hip exoskeleton at a speed of 5 km/h with 7 participants. Three muscles which include rectus femoris, tibialis anterior, and medial gastrocnemius are investigated in three conditions: user-preferred assistance (ASS), zero torque (ZT), and normal walking (NW). The results show that all muscles achieve an activity reduction in ASS mode compared with ZT or NW. Meanwhile, there is a statistically significant difference on medial gastrocnemius in ASS mode with respect to both ZT and NW (−15.63 ± 6.51% and −8.73 ± 6.40%, respectively).

Vision-based gait analysis for real-time Parkinson disease identification and diagnosis system

ABSTRACT Computer-assisted Parkinson’s disease-specific gait pattern recognition has gained more attention in the past decade due to its extensive application. In this research study, vision-based gait feature extraction is obtained from the observed skeleton points to support the real-time Parkinson disease prediction and diagnosis in the smart healthcare environment. So, a novel kernel-based principal component analysis (KPCA) is introduced for establishing respective feature extraction and dimensionality reduction on the patient’s video data. In this research study, a vision-based Parkinson disease identification system (VPDIS) is developed with a feature-weighted minimum distance classifier model to support the clinical assessment of Parkinson’s disease. At the time of experimentation, a steady-state walking style of the patient was captured using the cameras fixed in the smart healthcare environment. Then, the accumulated walking frames from the remote patients were transformed into the required binary silhouettes for the sake of noise minimisation and compression purpose. The resulting experimentation shows that the proposed feature extraction approach has significant improvements on the recognition of target gait patterns from the video-based gait analysis of Parkinson’s and normal patients. Accordingly, the proposed VPDIS using feature-weighted minimum distance classifier model provides better prediction time and classification accuracy against the existing healthcare systems that is developed using support vector machine and ensemble learning classifier models.

Gait Recognition with Self-Supervised Learning of Gait Features Based on Vision Transformers.

Gait is a unique biometric trait with several useful properties. It can be recognized remotely and without the cooperation of the individual, with low-resolution cameras, and it is difficult to obscure. Therefore, it is suitable for crime investigation, surveillance, and access control. Existing approaches for gait recognition generally belong to the supervised learning domain, where all samples in the dataset are annotated. In the real world, annotation is often expensive and time-consuming. Moreover, convolutional neural networks (CNNs) have dominated the field of gait recognition for many years and have been extensively researched, while other recent methods such as vision transformer (ViT) remain unexplored. In this manuscript, we propose a self-supervised learning (SSL) approach for pretraining the feature extractor using the DINO model to automatically learn useful gait features with the vision transformer architecture. The feature extractor is then used for extracting gait features on which the fully connected neural network classifier is trained using the supervised approach. Experiments on CASIA-B and OU-MVLP gait datasets show the effectiveness of the proposed approach.

Interpretable evaluation for the Brunnstrom recovery stage of the lower limb based on wearable sensors.

With the increasing number of stroke patients, there is an urgent need for an accessible, scientific, and reliable evaluation method for stroke rehabilitation. Although many rehabilitation stage evaluation methods based on the wearable sensors and machine learning algorithm have been developed, the interpretable evaluation of the Brunnstrom recovery stage of the lower limb (BRS-L) is still lacking. The paper propose an interpretable BRS-L evaluation method based on wearable sensors. We collected lower limb motion data and plantar pressure data of 20 hemiplegic patients and 10 healthy individuals using seven Inertial Measurement Units (IMUs) and two plantar pressure insoles. Then we extracted gait features from the motion data and pressure data. By using feature selection based on feature importance, we improved the interpretability of the machine learning-based evaluation method. Several machine learning models are evaluated on the dataset, the results show that k-Nearest Neighbor has the best prediction performance and achieves 94.2% accuracy with an input of 18 features. Our method provides a feasible solution for precise rehabilitation and home-based rehabilitation of hemiplegic patients.

Utilizing Spatio Temporal Gait Pattern and Quadratic SVM for Gait Recognition

This study aimed to develop a vision-based gait recognition system for person identification. Gait is the soft biometric trait recognizable from low-resolution surveillance videos, where the face and other hard biometrics are not even extractable. The gait is a cycle pattern of human body locomotion that consists of two sequential phases: swing and stance. The gait features of the complete gait cycle, referred to as gait signature, can be used for person identification. The proposed work utilizes gait dynamics for gait feature extraction. For this purpose, the spatio temporal power spectral gait features are utilized for gait dynamics captured through sub-pixel motion estimation, and they are less affected by the subject’s appearance. The spatio temporal power spectral gait features are utilized for a quadratic support vector machine classifier for gait recognition aiming for person identification. Spatio temporal power spectral preserves the spatiotemporal gait features and is adaptable for a quadratic support vector machine classifier-based gait recognition across different views and appearances. We have evaluated the gait features and support vector machine classifier-based gait recognition on a locally collected gait dataset that captures the effect of view variance in high scene depth videos. The proposed gait recognition technique achieves significant accuracy across all appearances and views.

Deep Learning enabled Fall Detection exploiting Gait Analysis.

Falls associated injuries often result not only increasing the medical-, social- and care-cost but also loss of mobility, impair chronic health and even potential risk of fatality. Because of elderly population growth, it is one of the major global public health problems. To address such issue, we present a Deep Learning enabled Fall Detection (DLFD) method exploiting Gait Analysis. More in details, firstly, we propose a framework for fall detection system. Secondly, we discussed the proposed DLFD method which exploits fall and non-fall RGB video to extract gait features using MediaPipe framework, applies normalization algorithm and classifies using bi-directional Long Short-Term Memory (bi-LSTM) model. Finally, the model is tested on collected three public datasets of 434x2 videos(more than 1 million frames) which consists of different activities and varieties of falls. The experimental results show that the model can achieve the accuracy of 96.35% and reveals the effectiveness of the proposal. This could play a significant role to alleviate falls problem by immediate alerting to emergency and relevant teams for taking necessary actions. This will speed up the assistance proceedings, reduce the risk of prolonged injury and save lives.

An Efficient Gait Abnormality Detection Method Based on Classification

In the study of human mobility, gait analysis is a well-recognized assessment methodology. Despite its widespread use, doubts exist about its clinical utility, i.e., its potential to influence the diagnostic-therapeutic practice. Gait analysis evaluates the walking pattern (normal/abnormal) based on the gait cycle. Based on the analysis obtained, various applications can be developed in the medical, security, sports, and fitness domain to improve overall outcomes. Wearable sensors provide a convenient, efficient, and low-cost approach to gather data, while machine learning methods provide high accuracy gait feature extraction for analysis. The problem is to identify gait abnormalities and if present, subsequently identify the locations of impairments that lead to the change in gait pattern of the individual. Proper physiotherapy treatment can be provided once the location/landmark of the impairment is known correctly. In this paper, classification of multiple anatomical regions and their combination on a large scale highly imbalanced dataset is carried out. We focus on identifying 27 different locations of injury and formulate it as a multi-class classification approach. The advantage of this method is the convenience and simplicity as compared to previous methods. In our work, a benchmark is set to identify the gait disorders caused by accidental impairments at multiple anatomical regions using the GaitRec dataset. In our work, machine learning models are trained and tested on the GaitRec dataset, which provides Ground Reaction Force (GRF) data, to analyze an individual’s gait and further classify the gait abnormality (if present) at the specific lower-region portion of the body. The design and implementation of machine learning models are carried out to detect and classify the gait patterns between healthy controls and gait disorders. Finally, the efficacy of the proposed approach is showcased using various qualitative accuracy metrics. The achieved test accuracy is 96% and an F1 score of 95% is obtained in classifying various gait disorders on unseen test samples. The paper concludes by stating how machine learning models can help to detect gait abnormalities along with directions of future work.

Foot-to-Ground Phases Detection: A Comparison of Data Representation Formatting Methods with Respect to Adaption of Deep Learning Architectures

Identifying the foot stance and foot swing phases, also known as foot-to-ground (FTG) detection, is a branch of Human Activity Recognition (HAR). Our study aims to detect two main phases of the gait (i.e., foot-off and foot-contact) corresponding to the moments when each foot is in contact with the ground or not. This will allow the medical professionals to characterize and identify the different phases of the human gait and their respective patterns. This detection process is paramount for extracting gait features (e.g., step width, stride width, gait speed, cadence, etc.) used by medical experts to highlight gait anomalies, stance issues, or any other walking irregularities. It will be used to assist health practitioners with patient monitoring, in addition to developing a full pipeline for FTG detection that would help compute gait indicators. In this paper, a comparison of different training configurations, including model architectures, data formatting, and pre-processing, was conducted to select the parameters leading to the highest detection accuracy. This binary classification provides a label for each timestamp informing whether the foot is in contact with the ground or not. Models such as CNN, LSTM, and ConvLSTM were the best fits for this study. Yet, we did not exclude DNNs and Machine Learning models, such as Random Forest and XGBoost from our work in order to have a wide range of possible comparisons. As a result of our experiments, which included 27 senior participants who had a stroke in the past wearing IMU sensors on their ankles, the ConvLSTM model achieved a high accuracy of 97.01% for raw windowed data with a size of 3 frames per window, and each window was formatted to have two superimposed channels (accelerometer and gyroscope channels). The model was trained to have the best detection without any knowledge of the participants’ personal information including age, gender, health condition, the type of activity, or the used foot. In other words, the model’s input data only originated from IMU sensors. Overall, in terms of FTG detection, the combination of the ConvLSTM model and the data representation had an important impact in outperforming other start-of-the-art configurations; in addition, the compromise between the model’s complexity and its accuracy is a major asset for deploying this model and developing real-time solutions.

Recognizing Gaits Across Walking and Running Speeds

For decades, very few methods were proposed for cross-mode (i.e., walking vs. running) gait recognition. Thus, it remains largely unexplored regarding how to recognize persons by the way they walk and run. Existing cross-mode methods handle the walking-versus-running problem in two ways, either by exploring the generic mapping relation between walking and running modes or by extracting gait features which are non-/less vulnerable to the changes across these two modes. However, for the first approach, a mapping relation fit for one person may not be applicable to another person. There is no generic mapping relation given that walking and running are two highly self-related motions. The second approach does not give more attention to the disparity between walking and running modes, since mode labels are not involved in their feature learning processes. Distinct from these existing cross-mode methods, in our method, mode labels are used in the feature learning process, and a mode-invariant gait descriptor is hybridized for cross-mode gait recognition to handle this walking-versus-running problem. Further research is organized in this article to investigate the disparity between walking and running. Running is different from walking not only in the speed variances but also, more significantly, in prominent gesture/motion changes. According to these rationales, in our proposed method, we give more attention to the differences between walking and running modes, and a robust gait descriptor is developed to hybridize the mode-invariant spatial and temporal features. Two multi-task learning-based networks are proposed in this method to explore these mode-invariant features. Spatial features describe the body parts non-/less affected by mode changes, and temporal features depict the instinct motion relation of each person. Mode labels are also adopted in the training phase to guide the network to give more attention to the disparity across walking and running modes. In addition, relevant experiments on OU-ISIR Treadmill Dataset A have affirmed the effectiveness and feasibility of the proposed method. A state-of-the-art result can be achieved by our proposed method on this dataset.

Multi-stream part-fused graph convolutional networks for skeleton-based gait recognition

Gait recognition, a task of identifying people through their walking pattern, has attracted more and more researchers' attention. At present, most skeleton-based gait recognition approaches extract gait features from merely joint coordinates. However, the information, e.g. bone and motion, is equally instructive and discriminative for gait recognition. Thus, this paper proposes a novel multi-stream part-fused graph convolutional network, MS-Gait, to fuse part-level information and capture multi-order features from skeleton data. To be specific, we integrate a channel attention learning mechanism into the graph convolutional networks (GCN) to improve the representational power. In addition, part-level information is merged by capturing features from the skeleton graph and its subgraphs concurrently. Finally, a multi-stream strategy is proposed to model joint, bone, and motion dynamics simultaneously, which is proven to effectively improve the recognition accuracy. On the popular CASIA-B dataset, extensive experiments demonstrate that our method can achieve state-of-the-art performance and is robust to confounding variations.

A unified perspective of classification-based loss and distance-based loss for cross-view gait recognition

Gait can be used to recognize people in an uncooperative and noninvasive manner and it is hard to imitate or counterfeit, which makes it suitable for video surveillance. The current solutions for gait recognition are still not robust to handle the conditions when the view angles of the gallery and query are different. We improve the performance of cross-view gait recognition from the perspective of metric learning. Specifically, we propose to use angular softmax loss to impose an angular margin for extracting separable features. At the same time, we use triplet loss to make the extracted features more discriminative. Additionally, we add a batch-normalization layer after extracting gait features to effectively optimize two different losses. We evaluate our approach on two widely-used gait dataset: CASIA-B dataset and TUM GAID dataset. The experiment results show that our approach outperforms the prior state-of-the-art approaches, which shows the effectiveness of our approach.

Comparative Study of Markerless Vision-Based Gait Analyses for Person Re-Identification

The model-based gait analysis of kinematic characteristics of the human body has been used to identify individuals. To extract gait features, spatiotemporal changes of anatomical landmarks of the human body in 3D were preferable. Without special lab settings, 2D images were easily acquired by monocular video cameras in real-world settings. The 2D and 3D locations of key joint positions were estimated by the 2D and 3D pose estimators. Then, the 3D joint positions can be estimated from the 2D image sequences in human gait. Yet, it has been challenging to have the exact gait features of a person due to viewpoint variance and occlusion of body parts in the 2D images. In the study, we conducted a comparative study of two different approaches: feature-based and spatiotemporal-based viewpoint invariant person re-identification using gait patterns. The first method is to use gait features extracted from time-series 3D joint positions to identify an individual. The second method uses a neural network, a Siamese Long Short Term Memory (LSTM) network with the 3D spatiotemporal changes of key joint positions in a gait cycle to classify an individual without extracting gait features. To validate and compare these two methods, we conducted experiments with two open datasets of the MARS and CASIA-A datasets. The results show that the Siamese LSTM outperforms the gait feature-based approaches on the MARS dataset by 20% and 55% on the CASIA-A dataset. The results show that feature-based gait analysis using 2D and 3D pose estimators is premature. As a future study, we suggest developing large-scale human gait datasets and designing accurate 2D and 3D joint position estimators specifically for gait patterns. We expect that the current comparative study and the future work could contribute to rehabilitation study, forensic gait analysis and early detection of neurological disorders.

Survey of Human Gait Analysis and Recognition for Medical and Forensic Applications

Gait is a behavioural biometric which sometimes changes due to diseases but it is still a strong identification metric that is widely used in forensic works, state biometric preserve sectors, and medical laboratories. Gait analysis sometimes helps to identify person’s present mental state which reflects on physiological therapy for improved biological system. There are various gait measurement forms which expand the research area from crime detection to medical enhancement. Many research works have been done so far for gait recognition. Many researchers focused on skeleton image of people to extract gait features and many worked on stride length. Various sensors have been used to detect gait in various light forms. This paper is a brief survey of works on gait recognition, collected from various sources of science and technology literature. We have discussed few efficient models that worked best as well as we have discussed about few data sets available.

Detection of mild cognitive Impairment from gait using Adaptive Neuro-Fuzzy Inference system

Early detection of mild cognitive impairment (MCI) among elders is very important for prevention of fast progress to probable Alzheimer’s disease. Studies show that motor abilities e.g. walking can be impaired for elderly people with mild to severe dementia and elders with MCI as well as memory defects. Thus, in this study, a novel method is presented to detect MCI among elders from skeletal data of elders’ gait recorded with a Kinect.V2 camera and using a proposed Adaptive Neuro-Fuzzy Inference System(ANFIS). The study included 70 elders with MCI and 80 healthy control (HC) elderly subjects who were selected based on specific criteria. Three sets of gait parameters as single-task walking (STW), dual-task walking (DTW), and cost of dual-task walking (DTC) were extracted from recorded gait tests. After comparing the extracted gait parameters using descriptive statistical methods, classification of elderly subjects into MCI and HC was done via the proposed ANFIS (Adaptive Neuro-Fuzzy Inference System) classifier using various extracted gait features. The results showed that cognitive tasks can dramatically increase the capability of gait analysis for the detection of MCI. Also, the comparison of the proposed ANFIS with several non-fuzzy classifiers showed that the ANFIS classifier can more accurately find the ambiguous boundary between MCI and HC in comparison to non-crisp methods. Generally, the proposed ANFIS classifier had values ​​of 93%, 90%, and 91.5% for accuracy, sensitivity, and F-score, respectively when all extracted gait parameters in the DTW condition for the classification of elderly subjects to MCI and HC groups.

Modeling Data-Driven Dominance Traits for Virtual Characters Using Gait Analysis.

We present a data-driven algorithm for generating gaits of virtual characters with varying dominance traits. Our formulation utilizes a user study to establish a data-driven dominance mapping between gaits and dominance labels. We use our dominance mapping to generate walking gaits for virtual characters that exhibit a variety of dominance traits while interacting with the user. Furthermore, we extract gait features based on known criteria in visual perception and psychology literature that can be used to identify the dominance levels of any walking gait. We validate our mapping and the perceived dominance traits by a second user study in an immersive virtual environment. Our gait dominance classification algorithm can classify the dominance traits of gaits with ˜73 percent accuracy. We also present an application of our approach that simulates interpersonal relationships between virtual characters. To the best of our knowledge, ours is the first practical approach to classifying gait dominance and generate dominance traits in virtual characters.

An Appearance Invariant Gait Recognition Technique Using Dynamic Gait Features

Gait recognition-based person identification is an emerging trend in visual surveillance due to its uniqueness and adaptability to low-resolution video. Existing gait feature extraction techniques such as gait silhouette and Gait Energy Image rely on the human body’s shape. The shape of the human body varies according to the subject’s clothing and carrying conditions. The clothing choice changes every day and results in higher intraclass variance and lower interclass variance. Thus, gait verification and gait recognition are required for person identification. Moreover, clothing choices are highly influenced by the subject’s cultural background, and publicly available gait datasets lack the representation of South Asian Native clothing for gait recognition. We propose a Dynamic Gait Features extraction technique that preserves the spatiotemporal gait pattern with motion estimation. The Dynamic Gait Features under different Use Cases of clothing and carrying conditions are adaptable for gait verification and recognition. The Cross-Correlation score of Dynamic Gait Features resolves the problem of Gait verification. The standard deviation of Cross-Correlation Score lies in the range of 0.12 to 0.2 and reflects a strong correlation in Dynamic Gait Features of the same class. We achieved 98.5% accuracy on Support Vector Machine based gait recognition. Additionally, we develop a multiappearance-based gait dataset that captures the effects of South Asian Native Clothing (SACV-Gait dataset). We evaluated our work on CASIA-B, OUISIR-B, TUM-IITKGP, and SACV-Gait datasets and achieved an accuracy of 98%, 100%, 97.1%, and 98.8%, respectively.

Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

A Lightweight Attention-Based CNN Model for Efficient Gait Recognition with Wearable IMU Sensors.

Wearable sensors-based gait recognition is an effective method to recognize people’s identity by recognizing the unique way they walk. Recently, the adoption of deep learning networks for gait recognition has achieved significant performance improvement and become a new promising trend. However, most of the existing studies mainly focused on improving the gait recognition accuracy while ignored model complexity, which make them unsuitable for wearable devices. In this study, we proposed a lightweight attention-based Convolutional Neural Networks (CNN) model for wearable gait recognition. Specifically, a four-layer lightweight CNN was first employed to extract gait features. Then, a novel attention module based on contextual encoding information and depthwise separable convolution was designed and integrated into the lightweight CNN to enhance the extracted gait features and simplify the complexity of the model. Finally, the Softmax classifier was used for classification to realize gait recognition. We conducted comprehensive experiments to evaluate the performance of the proposed model on whuGait and OU-ISIR datasets. The effect of the proposed attention mechanisms, different data segmentation methods, and different attention mechanisms on gait recognition performance were studied and analyzed. The comparison results with the existing similar researches in terms of recognition accuracy and number of model parameters shown that our proposed model not only achieved a higher recognition performance but also reduced the model complexity by 86.5% on average.

Learning Age From Gait: A Survey

Age is an important human attribute that needs to be determined for various purposes, including security, health, human identification, and law enforcement. Hence, there is an increasing research interest in automatic age estimation using biometric traits such as face and gait. In recent years, gait analysis has received growing attention due to the pervasive nature of video surveillance. Gait signals that measure the manner of walking can be obtained using vision and sensor-based techniques. Individual gait patterns obtainable from videos, images, or sensors are shown unconsciously and are not easily obscured. Additionally, gait signals can be obtained unobtrusively with cameras placed at a long distance because gait does not require high-resolution images. However, the extraction of age-associated gait features is a challenging task due to various gait covariates. These covariates include clothing and view changes for vision-based gait; walking slope and footwear for sensor-based gait. This paper provides a survey of scientific literature on age estimation using gait features. We focus on the approaches to extracting age-associated gait features, namely, vision-based and sensor-based approaches, how they may be affected by the different covariates, and domain-specific applications. To make this work useful for as wide of an audience as possible, we also include discussions on key topics such as existing datasets, evaluation strategies, and open challenges that should be addressed in the future.

An Efficient Automatic Gait Anomaly Detection Method Based on Semisupervised Clustering.

The aim of this work is to develop a common automatic computer method to distinguish human individuals with abnormal gait patterns from those with normal gait patterns. As long as the silhouette gait images of the subjects are obtainable, the proposed method is capable of providing online anomaly gait detection result without additional work on analyzing the gait features of the target subjects before ahead. Moreover, the proposed method does not need any parameter settings by users and can start producing detection results under the work by only collecting a very small number of gait samples, even though none of those gait samples are abnormal. Therefore, the proposed method can provide fast and simple deployment for various anomaly gait detection application scenarios. The proposed method is composed of two main modules: (1) feature extraction from gait images and (2) anomaly detection via binary classification. In the first module, a new representation of the most frequently involved area of the silhouette gait images called full gait energy image (F-GEI) is proposed. Furthermore, based on the F-GEI, a novel and simple method characterizing individual walking properties is developed to extract gait features from individual subjects. In the second module, based on the very limited prior knowledge on the target dataset, a semisupervised clustering algorithm is proposed to perform the binary classification for detecting the gait anomaly of each subject. The performance of the proposed gait anomaly detection method was evaluated on the human gaits dataset in comparison with three state-of-the-art methods. The experiment results show that the proposed method is an effective and efficient gait anomaly detection method in terms of accuracy, robustness, and computational efficiency.