# A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment

Authors: Anthony Bawa; Konstantinos Banitsas; Maysam Abbod
Journal: Journal of Healthcare Engineering
 (2021)

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## Abstract

Gait and posture studies have gained much prominence among researchers and have attracted the interest of clinicians. The ability to detect gait abnormality and posture disorder plays a crucial role in the diagnosis and treatment of some diseases. Microsoft Kinect is presented as a noninvasive sensor essential for medical diagnostic and therapeutic purposes. There are currently no relevant studies that attempt to summarise the existing literature on gait and posture abnormalities using Kinect technology. The purpose of this study is to critically evaluate the existing research on gait and posture abnormalities using the Kinect sensor as the main diagnostic tool. Our studies search identified 458 for gait abnormality, 283 for posture disorder of which 26 studies were included for gait abnormality, and 13 for posture. The results indicate that Kinect sensor is a useful tool for the assessment of kinematic features. In conclusion, Microsoft Kinect sensor is presented as a useful tool for gait abnormality, postural disorder analysis, and physiotherapy. It can also help track the progress of patients who are undergoing rehabilitation.


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## Body

## 1. Introduction

Microsoft’s Kinect sensor is a motion-sensing device that gives users the features to interact with game consoles and computers via ways such as gestures, spoken commands, or movement [1]. Kinect sensors provide new and enhanced features for motion detection and 3D reconstruction. Kinect sensors also introduce many features that allow for more accurate research into the movement of the human body and its gestures. The sensors allow for interaction through voice commands that is a unique component of the technology. It has detectors and infrared emitters to capture human physical activities.The key components of Microsoft Kinect sensors are the RGB cameras, IR depth sensors, and the multiple microphone array. The second version of Kinect has some enhanced features compared to earlier Kinect [2]. The colour camera of Kinect v2 has 1,920 × 1,080 @30fps while that of Kinect v1 has 640 × 480 @30fps. In terms of the depth camera capabilities, Kinect v2 uses 512 × 424 pixels, while Kinect v1 uses 320 × 240 pixels, and as a result, Kinect v2 has better image recognition compared to the earlier version. Kinect v2 is noted to have a wider area view compared to Kinect v1. Another key feature is that Kinect v2 has better skeletal joint tracking where it is able to capture 26 joints, whereas Kinect v1 can only capture 20 joints. The unique feature of Kinect sensors can be applied to the medical field for the purposes of diagnosing diseases and physiotherapy rehabilitation of people who may have walking disabilities due to physical injury or related diseases.As stated above, Kinect has found application in many areas related to posture and motion capturing. The major bulk of studies are related to Kinect research in the areas of motion tracking, monitoring, diagnosis, and rehabilitation. Some representative studies with Kinect technology include: Lavanya et al. [3] presented dynamic finger gestures with skeletal data extracted from the depth sensor. A unique technique was designed for the recognition of dynamic gestures that can be used in auditoriums and classrooms. This approach allowed for more dynamic hand gestures to be developed that can be used in different environments. An example is a tutor using this technique to instruct students in a classroom who have speech problems to assist in their studies.The use of Kinect for medical monitoring and diagnosis has also been trialed by researchers. Ales Prochazka et al. [4] presented a novel technique of using Kinect for heart rate estimation and breath monitoring to determine the likelihood of any medical condition. The mean thorax movement was monitored within a selected area to estimate the breathing of patients. Huy-Hieu et al. [5] presented a real-time system for the detection of objects for patients who are visually impaired. A unique system was designed that allowed visually impaired people to move freely and to detect any obstructions. Object detection was based on the 3D information captured with a depth sensor. However, the designed system was limited to only indoor use. Xin Dang et al. [6] presented a novel interactive system with an electroencephalogram and depth sensor for people with dementia. Skeletal data captured from the depth sensor were extracted to determine the motion of a user and their mental state. The designed system using a deep neural network can be used to aid patients with dementia. Torres et al. [7] provided a novel approach to assist physicians in the diagnosis of Parkinson’s disease using posture and movement captured with Kinect. The characteristics of movements such as frequency and amplitude were essential to study tremors in people with Parkinson’s. The results achieved in the study can assist clinicians to diagnose Parkinson’s based on the tremors intensity and the postural changes.Kinect sensors are also widely used for the purposes of rehabilitation. Capecci et al. [8] demonstrated an innovative approach in the evaluation of dynamic movement in a rehabilitation scenario. They were able to track skeletal joints in evaluating the performance of patients during a low back pain physiotherapy exercise. Postolache et al. [9] developed a unique framework for physiotherapy assessment based on a mobile application using skeletal data. The designed system assists physiotherapists to improve the effectiveness of the training sessions for patients undergoing rehabilitation. Monique Wochatz et al. [10] illustrated a reliable and valid assessment of the lower extremity rehabilitation of exercises using Kinect v2 sensors. The authors demonstrated the Kinect sensor as a reliable tool in assessing the lower limb position and the joint angles during exercises. Sanjay et al. [11] developed a unique framework for stroke rehabilitation of patients in a home environment. A framework was designed to aid patients who have suffered a stroke in their treatment process. The designed system can be used indoors to help patients who have difficulty in movement.Abnormal gait is the asymmetric movement of a person that is most likely to be caused by disease or physical injuries. This could be a result of nerves damage, injuries, weakness of muscles, or joint problems. The detection of gait abnormalities at an early stage can help prevent other complications. There are traditional wearable sensors for gait abnormal detection; however, these conventional methods are quite cumbersome to use. Kinect is seen as a better alternative to wearable sensors in gait abnormal detection.As such, gait analysis has also received wide interest among researchers [12–14]. Kinematic features such as the step length, walking speed, and cadence are useful to determine the gait of an individual. This will normally be cyclical and symmetric unless there are some forms of abnormalities. The human gait could be affected by the musculosketal and neurological systems as well as the motions habits [15]. Hassain Bari et al. [16] presented a novel method by designing a deep neural network for gait recognition. This was then evaluated with a 3D skeletal gait data set. Another study by Wan Zharfan et al. [17] illustrated an economic technique of gait analysis based on pixel coordinates of body joints. The technique served as an alternative method to determine gait parameters in a Vicon motion analysis.The human posture is the physical positioning by which the body takes at a particular time. Posture is the arrangement of the structure of the human body and its position. Correct body posture can help reduce pressure on the human body by keeping it balanced. The human body posture may be intentional or unintentional due to natural causes. There are several techniques for postures recognition with skeletal data. Samiul Monir et al. [18] presented a novel technique with a rotation and scale-invariant for posture recognition from skeletal features. This technique for posture recognition used skeletal data and angle rotation of an individual. A set of vectors and manipulation of angles were used to determine the posture. Zequn et al. [19] developed a novel posture recognition model that can be used to identify different postures captured. The depth sensor was used to generate features of different body parts of an individual. The captured features were then fed into the support vector machine (SVM) to identify the posture.Although there are quite a number of studies available using Kinect for the assessment of gait and posture abnormalities, to the best of our knowledge, there is no overall review study that attempts to summarise articles on the use of the Kinect sensor for gait abnormality and posture disorder assessment. Our review adopts a systematic approach similarly used by Shmuel Springer et al. [20]. The study provides up-to-date review of the articles for analysis and discussion.


## 2. Methods

In this section, we retrieve articles that meet the inclusion criteria for our study. The existing articles identified were summarised into tables indicating the methods used and the sampling area. The sample area stated the source from which the article was retrieved, the authors of the article, and the year it was published. Our focus was on articles that used Kinect sensors for assessing gait and posture abnormalities.


### 2.1. Search and Identification of Articles

The scholarly database used to identify the articles were: IEEE Xplore, ScienceDirect, CINAHL plus, and PubMed. The search was done in two different parts as follows:(i)
Part 1: the use of Kinect sensors for the detection of gait abnormality or disorder(ii)
Part 2: the use of Kinect sensors for detection posture disorder or instabilityThe key terms used in part 1 of the search were: “Kinect sensor,” “gait abnormality,” “gait disorder,” and “walking abnormality.” In the second part, the key terms used were: “Kinect sensor,” “posture disorder,” “posture abnormality,” and “posture instability.” The search was conducted between January and May 2021 to retrieve the most recent articles.


### 2.2. Eligibility Criteria of the Identified Articles

In identifying the articles for the study, we conducted two different searches for the database by two independent authors. The authors were able to identify and remove duplicate articles from the various database. The elimination of irrelevant articles was done without bias or oversight in order to get all relevant articles that meet the inclusion criteria. A number of diseases that could affect gait and posture abnormalities were included. They were: Parkinson’s, ataxia, multiple scoliosis, stroke, and depression.The exclusion criteria were for articles that only discussed gait detection and postures without considering abnormality. In the second part, articles that only discuss posture assessments without examining posture disorder or deformity were also not included. Articles that used wearable sensors for gait abnormality and posture disorder detection were not included. Figure1 is a flowchart diagram of the search methodology.Figure 1
Flowchart for the search methodology of articles included.


## 2.1. Search and Identification of Articles

The scholarly database used to identify the articles were: IEEE Xplore, ScienceDirect, CINAHL plus, and PubMed. The search was done in two different parts as follows:(i)
Part 1: the use of Kinect sensors for the detection of gait abnormality or disorder(ii)
Part 2: the use of Kinect sensors for detection posture disorder or instabilityThe key terms used in part 1 of the search were: “Kinect sensor,” “gait abnormality,” “gait disorder,” and “walking abnormality.” In the second part, the key terms used were: “Kinect sensor,” “posture disorder,” “posture abnormality,” and “posture instability.” The search was conducted between January and May 2021 to retrieve the most recent articles.


## 2.2. Eligibility Criteria of the Identified Articles

In identifying the articles for the study, we conducted two different searches for the database by two independent authors. The authors were able to identify and remove duplicate articles from the various database. The elimination of irrelevant articles was done without bias or oversight in order to get all relevant articles that meet the inclusion criteria. A number of diseases that could affect gait and posture abnormalities were included. They were: Parkinson’s, ataxia, multiple scoliosis, stroke, and depression.The exclusion criteria were for articles that only discussed gait detection and postures without considering abnormality. In the second part, articles that only discuss posture assessments without examining posture disorder or deformity were also not included. Articles that used wearable sensors for gait abnormality and posture disorder detection were not included. Figure1 is a flowchart diagram of the search methodology.Figure 1
Flowchart for the search methodology of articles included.


## 3. Results

The initial search and retrieval of all articles from the various database were 458 for gait and 283 for posture, all using Kinect technology. The articles were further screened to ensure they meet the eligibility criteria for this study. In the end, a total of 26 articles were included for gait abnormality and 13 articles for posture disorders.Table1 summarises all the studies that were included for the review for gait abnormality, the journals where the articles were retrieved and the year of publications. In Table 1, the methodology describes the sampling method applied, the statistical method, and the descriptive approach used. That sampling method describes the number of participants in the study, gender, and age distribution. The disease associated with the abnormality was stated. The sampling methods from the reviewed articles were categorised as fully stated, partially, or not stated. The statistical method describes the statistical techniques that were used in the analysis of the data. The statistical methods were either sufficiently used or partially used. It describes the models and mathematical equations that were used in the analysis of skeletal data. The description method used refers to capturing of the skeletal data, processing of data, algorithms used, and the analysis of the results. It also includes the tools used in the analysis of results and a detailed discussion of the findings. Finally, the description method was either adequate or partial description.Table 1
Reviewed articles on gait abnormality or disorder.
Sample study areaMethodology used for the reviewed articlesAuthorsJournal/conference paperYear of publicationSampling method in studyStatistical methodDescription methodBei et al. [21]IEEE sensors journal2018Partially statedSufficiently usedAdequate descriptionWang et al. [22]IEEE sensors journal2019Fully statedSufficiently usedAdequate descriptionTsukagoshi et al. [23]Journal of clinical neuroscience2019Fully statedSufficiently usedPartial descriptionAmin amini et al. [24]Journal of healthcare engineering2019Fully statedSufficiently usedAdequate descriptionProchazka et al. [25]Elsevier – digital signal processing2015Partially statedPartially usedPartial descriptionPachón-Suescún et al. [26]International journal of electrical and computer engineering (IJECE)2020Partially statedPartially usedPartial descriptionGholami et al. [27]IEEE journal of biomedical and health informatics2016Fully statedPartially usedAdequate descriptionMaxime devanne et al. [28]International conference on pattern recognition (ICPR)2016Not statedNot usedAdequate descriptionLatorre et al. [29]Elsevier – journal of biomechanics2018Fully statedPartially usedAdequate descriptionPrakash et al. [30]IEEE transactions on instrumentation and measurements2021Fully statedPartially usedAdequate descriptionNguyen et al. [31]Sensors, MDPI2016Partially statedSufficiently usedAdequate descriptionShrivastava et al. [32]Elsevier – materials today: Proceedings2020Partially statedPartially usedPartial descriptionProchazka et al. [33]IEEE international conference on image processing (ICIP)2014Partially statedPartially usedPartial descriptionFang et al. [34]IEEE access on multiphysics2019Fully statedSufficiently usedAdequate descriptionIsmail et al. [35]IEEE international conference on advances in biomedical engineering (ICABME)2017Partially statedNot usedPartial descriptionAmini et al. [36]Disability and rehabilitation: Assistive technology2018Fully statedSufficiently usedAdequate descriptionElkholy et al. [37]IEEE journal of biomedical and health informatics2019Not statedPartially usedPartial descriptionSoltaninejad et al. [38]Sensors, MDPI2019Fully statedPartially usedAdequate descriptionKozlow et al. [39]Sensors, MDPI2018Fully statedSufficiently usedAdequate descriptionChakraborty et al. [40]International conference on computational science2020Partially statedPartially usedPartial descriptionJyothsna et al. [41]IEEE engineering in medicine and biology society (EMBC)2020Partially statedNot usedPartial descriptionWon et al. [42]IEEE engineering in medicine and biology society (EMBC)2019Not statedNot usedPartial descriptionJinnovart et al. [43]IEEE conference on decision and control (CDC)2020Not statedNot usedPartial descriptionElkholy et al. [44]International conference of the IEEE engineering in medicine and biology society (EMBC)2020Fully statedNot usedAdequate descriptionMeng et al. [45]Joint conference on computer vision, imaging and computer graphics theory and applications2016Not statedNot usedPartial descriptionJun et al. [46]IEEE access2020Not statedNot usedPartial descriptionThe approach used in Table1 was also similarly adopted in Table 2 except that it summarised the various articles for identifying posture disorders using Kinect. The sampling methods, statistical methods, and the description method are also indicated in this table.Table 2
Reviewed articles on posture abnormality or disorder.
Sample study areaMethodology used for the reviewed articlesAuthorsJournal/ConferenceYear of publicationSampling method in studyStatistical methodsDescription of model usedFerrais et al. [47]Sensors, MDPI2019Fully statedSufficiently usedAdequate descriptionJawed et al. [48]IEEE international conference on emerging trends in engineering, sciences and technology2019Not statedNot usedPartial descriptionYang et al. [49]IEEE sensors2014Fully statedSufficiently usedPartial descriptionCastroa et al. [50]Elsevier porto biomedical journal2016Fully statedPartially usedAdequate descriptionChin-hsuan et al. [51]Sensors, MDPI2020Fully statedSufficiently usedAdequate descriptionAbobakr et al. [52]IEEE international conference on systems, man, and cybernetics2017Partially statedNot usedPartial descriptionNapoli et al. [53]Biomedical engineering society2017Partially statedPartially usedPartial descriptionMeng-Che shih et al. [54]Journal of neuro engineering and rehabilitation2016Fully statedSufficiently usedAdequate descriptionChanpimol et al. [55]Archives of physiotherapy2017Partially statedPartially usedPartial descriptionBortone et al. [56]IEEE-EMBS international conference on biomedical and health informatics2014Not statedNot usedPartial descriptionModesto et al. [57]Elsevier applied ergonomics2017Partially statedNot statedPartial descriptionNorbert et al. [58]Health informatics meets eHealth2017Fully statedFully statedAdequate descriptionRose et al. [59]Elsevier: gait and posture2012Partially statedNot statedPartial descriptionIn Table3, the details of each article included in the study were categorised into two major phases. The first phase deals with the sampling technique used in each study while the second phase describes the key gait features captured with the major findings of each study. The limitations for each study were also included in the table.Table 3
Detailed features of articles on gait abnormality or disorder.
Sampling techniquesKey gait features and aims of the identified articlesAuthorsGender and age range of participantsAbnormality or diseaseKinect sensor versionData type captureGait parameters measuredData analysis toolAlgorithm usedAccuracy achieved (%)Major findingsLimitations of studyBei et al. [21]Gender and age not stated70 normal walking and 50 walking disorderKinect v2Skeletal dataLeg swing angle (deg), knee and ankle joint angle (deg), step length (m), gait cycle (deg)Not statedK-means algorithms, Bayesian algorithmsNot statedA novel technique was designed to demonstrate movement disorder through gait symmetry analysisSome key gait parameters and joint angle were not considered. Only a small data set was used to test the modelWang et al. [22]98 individuals; gender and age not statedDepressionKinect v2Skeletal dataGait velocity (m/s),Joint angles (deg)MATLABt-SNE algorithm93.75A nonintrusive framework was designed to detect depressionSome gait features are required to improve the robustness of the model in a real environmentTsukagoshi et al. [23]Ataxia (male = 14 female = 11); age 54.1 ± 14.6 years. Parkinson’s (male = 10 female = 15); age 68.4 ± 8.1 years. Healthy people (male = 13 female = 12), age 62.0 ± 13.9 years25 Patients with ataxia, 25 patients with Parkinson’s, and 25 health peopleKinect v2Skeletal dataStride length (m), feet length (m), gait rhythm, (m/s)SPSS package suitClinical scaleNot statedKinect depth sensor to quantitatively evaluate gait interference for patients who have a movement disorderBody joint gaits angulation were not considered and thus there may be less precision with this modelAmini et al. [24]15 participants (12 male and 3 female); average age 54–92 yearsPeople with Parkinson’sKinect v2Skeletal dataGait cycle (deg), knee angle (deg), number of footsteps (m)Not statedHeuristic fall detection algorithmNot statedA unique model was designed to detect freeze of gait for people with Parkinson’sThe developed system is limited to only thex-axis for the freeze of gait detectionAleš prochazka et al. [25]51 individuals; gender not stated; age: Parkinson’s, 52–87 years, healthy mature: 32–81 years, young: 23–25 years18 Individuals with Parkinson’s, 18 healthy matured, and 15young onesKinect v1Not statedStep length (m), gait length (m/s), stride length (m)MATLABBayesian algorithm94.1A novel technique was developed using Bayesian classification algorithm to recognise gaits disorder for people with Parkinson’s diseaseSome key joint angles were excluded in developing an abnormal gait recognition model and thus not so efficientCesar et al. [26]Gender and age not statedNot statedKinect v2Skeletal dataStep speed (m/s), stride speedMATLABLSTM algorithm98.1A model was designed to detect gait abnormality using the LSTM algorithmBody joint angles are required to test and improve the accuracy of the modelGholami et al. [27]MS (male = 1, female = 9); age = 41–79 years. NP (male = 1, female = 9); age 36–80 years10 multiple scoliosis and 10 normal peopleKinect v2Skeletal dataGait velocity (m/s), stride length (m), stride time (s), step time (s)Not statedEDSS algorithm and MSWS algorithmNot statedA novel framework was designed to evaluate the gaits abnormality of people with multiple scoliosisThe designed framework does not provide enough reliability to detect the disease. There is less accuracy with the designed model.Devanne et al. [28]Gender and age not statedNot statedKinect v2Skeletal dataStep length (m), body joint angles (deg)Not statedRiemannian manifold algorithmNot statedA model is designed to detect gait abnormality using motion trajectoriesThe method is not able to identify static gait abnormality such as a freeze of gaitLatorre et al. [29]45 healthy individuals (men = 31, women = 14); age 30.6 ± 7.6 years. 38 stroke survived people (men = 22, women = 16); age = 56.1 ± 13.2 years45 Healthy individuals and 38 stroke surviving peopleKinect v2Skeletal dataGait speed (m/s), stride length (m), stride time (s), swing time (s), step time (s), step asymmetryMATLABBayesian algorithmNot statedThe authors illustrated the reliability of using Kinect-based methods to estimate gait disorder for poststroke adult individualsThe method used in the study were limited which influenced some errors with the gait parameters measuredPrakash et al. [30]24 individuals (13 males and 11 females); age not statedNot statedKinect v2Skeletal dataLeft knee angle (deg), right knee angle (deg)Not statedIR-UMB algorithmNot statedA model was developed to detect gait abnormality using contactless IR-UWBOnly the knee angle was considered and this may not give accuracy of the designed modelNguyen et al. [31]20 individuals; gender and age not stated10 healthy people and 10 abnormal (Parkinson’s/stroke)Kinect v2Skeletal dataLeft hip angle (deg), right hip angle (deg), left knee angle (deg), right knee (deg), left ankle (deg), right ankle (deg)MATLABHMM algorithm90.12A novel approach was designed for gait abnormality detection using skeletal-based data with no prior knowledge of individual gaitThe method used in the study provided enough precision of the results achievedShrivastava et al. [32]24 individuals; gender and age not stated12 Normal walking and 12 abnormal walkingKinect v1Skeletal dataStep length (m), gait cycle (s), hip left foot angle (deg), right foot angle (deg)MATLABKNN algorithm, SVM algorithm, and decision tree algorithm83.33The authors developed a model using machine learning for gaits abnormality detection using data from KinectThe model used does not provide high precision and efficiency for detecting gait abnormalityProchazka et. Al [33]36 individuals; gender not stated; people with Parkinson’s: age = 52–87 years, healthy control: age = 32–81 years18 Individuals with Parkinson’s. 18 healthy controlKinect v1Skeletal dataStride length (m),MATLABSkeletal tracking algorithm90A system was designed to detect Parkinson’s disease based on the gaits features. This could be used for early detection of Parkinson’s.Only a few parameters such as stride length were used and this does not provide enough efficiency and reliability of the modelFang et al. [34]3,669 individuals (1555 males and 2114 females); age range 22–28 yearsSuspected cases of depressionKinect v2Skeletal dataWalking speed (m/s), arm swing (mm), stride length (m), vertical head position (deg)MATLABSVM algorithm; KNN, RF, and LR algorithms; linear discriminant analysis (LDA)91.58A novel model was designed using different ML to detect depression prevalence among studentsDifferent viewing points were not considered in developing a model to detect depressionIsmail et al. [35]11 individuals; gender not stated; age = 21–25 years11 healthy control individualsKinect v2Skeletal dataGait speed (m/s), stride length (m), right knee angle (deg), left knee angle (deg), right ankle (deg), left ankle (deg), hip angle (deg)MATLABAngle average algorithmNot statedA novel system is developed to determine gait abnormalities using gait cycleThere were some marginal errors in the data set used that does not provide enough efficiency of the model used in detecting abnormalityAmin amini et al. [36]11 healthy subjects; age range 24–31 yearsNot statedKinect v2Skeletal dataBody angle (deg), feet/joint distanceNot statedPythagorean theoremNot statedA unique model is designed for casting automatic/dynamic visuals for people with Parkinson’s diseaseThe designed model is limited to indoor environment useElkholy et al. [37]Gender and age not statedNot statedKinect v2, Asus Xtion PROSkeletal dataGait speed (m/s), gait cycle (deg), stride length (m)Not statedOC-SVM algorithm and IF algorithmNot statedA new approach was designed to detect gait abnormality based on unsupervised gait energy image (GEI)Some factors that could affect the accuracy of abnormal gaits detection based on the GEI were not consideredSaltaninejad et al. [38]5 individuals (4 males and 1 female); average age = 30.8 yearsNot statedKinect v2Skeletal dataGait speed (m/s), hip angle (deg), knee angle (deg)Not statedBest removal algorithm for FOG90An automatic and fast assessment for FOG was designedThe model needs to be tested with real P.D patients to improve its reliabilityKozlow et al. [39]21 Males and 9 females; age = 25 ± 5.2 years28 Healthy individualsKinect v2Skeletal dataCadence (step/min), left angle joint angle (deg), left knee joint angle (deg), right angle joint angle (deg), right knee joint angle (deg), left stride (deg), right stride length (deg)MATLABBayesian algorithm88.68The authors demonstrated the use of Bayesian network algorithm to classify gait abnormalityThere are some limitations to the robustness and accuracy of this framework in detecting gait abnormalityChakraborty et al. [40]15 individuals; gender and age not statedPatients with cerebral palsyKinect v2Skeletal dataGait cycle (deg), left ankle (deg), right ankle (deg)MATLABDempster shafer classifier87.5A novel technique is designed based on automated gait to detect gait abnormality for patients with cerebral palsyThis technique could be challenging in a real environmentJyothsna et al. [41]20 individuals; gender not stated; age = 80 years and above20 people making up the various group, cognitive healthy individuals (CHI), subject cognitive impaired (SCI), and possible mildly cognitive person (pMCI)Kinect v2Skeletal dataStride length (m), mean stride (m), step time (min), cadence (m), gait velocity (m/s)Not statedConvolutional neural network algorithmNote statedA framework was designed to detect the gait abnormality for patients with dementiaMore gaits parameters need to be extracted to test the model on a large data set for dementia patients to improve the efficiencyDeok-won et al. [42]Gender and age not statedNot statedKinect v2Skeletal dataStride length (m), lower limbs body joint angles (deg)Not statedRNN-LSTM algorithm97The abnormal gait recognition model was designed that is capable of recognising five different abnormal gaits patterns using multiple Kinect sensorsThe challenge with this model is that it can only recognise abnormal gait that were used in the training of the RNN-LSTM model. Some abnormal gaits may not be recognised.Jinnovart et al. [43]Gender and age not statedNot statedKinect v2Skeletal dataStride length (m), body joint anglesNot statedRNN algorithm, LSTM algorithm, and GRU algorithmRNN = 73.4, LSTM = 82.8, and GRU = 81.6A real-time recognition of abnormal gait was presented using recurrent neural networkSome abnormal gait may not be recognised with the designed modelAmr et al. [44]43 individuals; gender for abnormal gait: male = 19 and female = 13; age = 18–85 years. Healthy control people (male = 8 and female = 3); age = 27–64 years32 Patients with gait abnormality and 11 healthy control peopleKinect v2, Asus Xtion PROSkeletal dataGait cycle (deg), swing phase(deg), step length (deg)Not statedOC-SVM algorithm and IF algorithmNot statedA robust and efficient skeletal system was developed to detect abnormal activities performed by a personThe model will require a large data set to test the efficiency of the designed modelMenget al. [45]Gender and age not statedNot statedKinect v2Skeletal dataInterskeletal joint distanceNot statedRandom forest classifierNot statedA system was developed using a skeletal inter-joint distance to detect abnormal gait and normal gaitThe developed system may not be robust because only a few gait features were used on small data sets for abnormal gait detectionJun et al. [46]9 individuals; gender and age not stated1 normal person and 8 abnormal gaitKinect v2Skeletal dataLeft hip angle (deg), right hip angle (deg), left knee angle (deg), right knee (deg), left ankle (deg), right ankle (deg)Not statedRNN algorithm and LSTM algorithmNot statedAn extraction method feature was developed using RNN to increase the performance of gait abnormality from a skeletal base systemA small data set was used to test the model, and this does not provide high efficiency for gait abnormal detectionRNN: recurrent neural network; LSTM: long short-term memory; GRU: gated recurrent units; OC-SVM: one-class support vector machine; IF: isolation forest; HMM: hidden Markov model; and KNN: k-nearest neighbors.A total number of 26 articles were reviewed. Most of the articles stated the number of participants in the study except in [26,28,37,42,43,45]. The majority of the studies used Kinect v2 as the main tool for capturing skeletal data for gait abnormality assessment, while a few articles used the older Kinect v1. In [37,44], the Asus Xtion PRO was used as a gold standard with a Kinect sensor for capturing skeletal data. Most of the reviewed articles did not state the data analysis tool. However, in [22,25,29,31,35,39,40], MATLAB was explicitly stated as the main tool for data analysis. In [23], the SPSS package was used as the data analysis tool. The gait features captured were mainly the step/stride length (m), stride time (s), gait speed (m/s), gait cycle (deg), gait rhythm (m/s), and step time (s). Some of the key joint angles measured were the hip, knee, and the ankle. Various algorithms were used to train the models for gait abnormality detection.A total number of 13 articles were included for the assessment of posture abnormality or disorder. Some of the reviewed articles [42,46,50,51] did not state the participants in the studies. In Table 4, Kinect v2 was mostly used for skeletal data capturing, except in a few studies that used Kinect v1. Nine of the reviewed articles used Kinect v2 while four used Kinect v1. Most of the studies did not state any medical condition that resulted in posture abnormality. However, in [47], Parkinson’s disease was stated, and in [56], a case of chronic traumatic brain injury was present. In [50,58], patients with suspected multiple scoliosis were also assessed for posture abnormality. The majority of the reviewed articles did not state the data analysis tool except in [47,48,50] that stated MATLAB. In [58], the IBM Watson Analytics was used to analyse the data for posture abnormality for patients with suspected cases of multiple scoliosis.Table 4
Detailed features of articles on posture abnormality or disorder.
Sampling techniquesKey body features and aims of the identified articlesAuthorsGender and age of participantsAbnormality/diseaseKinect sensor versionData type captureBody features measuredData analysis toolAlgorithm usedMajor findingsLimitations of the studyFerrais et al. [47]14 individuals (8 male and 6 female); age = 53–80 yearsParkinson’s diseaseKinect v2Skeletal dataCentre of body mass (m)MATLABKNN algorithmA system is designed for the automatic posture analysis of people with Parkinson’s to determine postural instabilityMore subjects are required to test the model to improve its reliability and efficiencyJawed et al. [48]Gender and age not statedNot statedKinect v1Skeletal dataBody joint angles (deg), body position (m)MATLABPattern recognition neural algorithmA system was developed using pattern recognition neural network model that is capable of analysing the whole body of a patient to determine if there is any postural disorderNot much efficiency with this model in detecting postural disorder with high accuracyYang et al. [49]18 individuals, 9 males and 9 females; age = 24.0 ± 0.7 yearsNot statedKinect v2Skeletal dataCentre of body mass (m)Not statedRSM algorithmA system was designed to evaluate the standing balance to determine posture instabilityThere may be some variations in the calibration to measure the COMCastroa et al. [50]98 individuals (males = 50 and females = 48) average; age 24.7 yearsSuspected scoliosis diseaseKinect v2Skeletal dataShoulder angulations (deg)MATLABSA methodThe Kinect sensor was used to quantitatively evaluate the posture of the spine to determine if there is any posture instabilityThe challenge was that the S2’s spinal exposition process was unassuredChin-Hsuan Liu et al. [51]45 individuals (15 youth and 30 elderly); age of youth = 24.06 ± 2.02 years; age of elderly = 71.13 ± 4.56 yearsNot statedKinect v2Skeletal dataBody joint angles (deg), center of body mass (m)Not statedMediolateral (ML) algorithmA system was designed to investigate the postural instability using the body joint coordination patternsOnly the mediolateral (ML) motion direction is considered in determining impairments of an individualAbobakr et al. [52]Gender and age not statedNot statedKinect v2Skeletal dataBody joint angles (deg)Not statedConVnet algorithm, AlexNet CNN algorithm, and RULA methodA system was developed using Kinect and for the early detection of postural work-related disorders for people in a manufacturing industryThe methods used only considered the joint angles in designing the model and thus the may not be enough precisionAlessandro Napoli et al. [53]15 individuals (7 male and 8 female); age not statedNot statedKinect v2Skeletal data3D position of body distance (m), spine angle (deg)Not statedBalance detection algorithmA system was designed to determine balancing deficits and postural instability of individualsThere should be an expansion of the features of automatic assessment of postural stability in determining the postural instabilityMeng-Che shih. Et l. [54]Gender BBE (male = 9 and female = 1); gender BT (male = 7 and female = 3); age BBE group 67.5 ± 9.96 years; age BT group 68.8 ± 9.67Individuals with Parkinson’s disease balance-based exergaming group (N = 10), balance training group (N = 10)Kinect v2Skeletal dataLimit of stability (LOS), one leg stance (OLS)Not statedBBS methodThe authors used a novel technique to assess the postural stability of individuals with Parkinson’s diseaseThe sample size was small, and calibration variability was observed in the exergaming sessionChanpimol et al. [55]1 individual (1 male); age = 37 yearsChronic traumatic brain injury (TBI)Kinect v2Skeletal dataBody position distance (m)Not statedLimits of stability (LOS) algorithmA study to improve the dynamic balance of an individual with TBI and improve the postural instability.The designed system is limited to a single individual with TBIBortone et al. [56]Gender and age not statedNot statedKinect v1Skeletal dataJoint angles (deg)Not statedNonasymmetric patternAn innovative system was designed to identify postural abnormalities using a two-stage approachThe body features measured do provide enough reliability and precision in detecting postural abnormalityModesto et al. [57]Gender and age not statedNot statedKinect v2Skeletal dataBody position (m), joint angles (deg), motion sequence (deg)Not statedRULA methodA system was developed using Kinect v2 to detect awkward postures in real timeThis designed model needs further investigation to determine its behaviour in a real working environmentNorbert et al. [58]30 individuals (male = 18 and Female = 12); average age = 16 years30 students suspected of scoliosisKinect v1Skeletal dataHeight measurement of hips and shoulders, angle of hips and shoulder (deg)IBM Watson analyticsNonirradiate body tracking methodThe detection of scoliosis from students due to their incorrect postureThe confusion matrix used showed Kinect sensor may not provide accurate screening of data capturedRose A. et al. [59]20 individuals; gender and age not stated20 healthy subjectsKinect v1Skeletal dataBody joint angles, knee joint, ankle joint, lateral/anterior joint anglesNot statedRegression algorithmA postural control assessment to determine those with postural control and those with postural imbalanceMeasuring the internal and external joints rotations had limitations, and thus, there are some variations of the resultsBBS: Berg balance scale; CNN: convolutional neural networks; RULA = rapid upper limb assessment; and SA: shoulder angulation.


## 4. Discussion

### 4.1. Gait Abnormality or Disorder

The purpose of this study is to review the available studies using Microsoft Kinect for the assessment of gait and posture abnormalities. The key features measured included the angles formed by leg swing, speed, and distance of each gait step. These parameters were useful in detecting gait asymmetry in order to distinguish normal from abnormal gait. Some other components from the summarised studies were the algorithms used, the major findings of each study, and limitations.From the reviewed articles, various methods were employed in assessing and detecting gait abnormality. The gait features captured were mainly the step/stride length (m), gait speed (m/s), gait cycle (deg), and step time (s). Some other gait features captured from the studies were angles of knee joints, ankle joints, and hip angles joints. The measured joint angles were used to train the models in detecting gait abnormalities. The measurement of joint angles helped improve the efficiency and robustness of the trained models to detect gait abnormality.They were various algorithms used to train the models for gaits abnormality detection. Some of the algorithms used in Table3 were machine learning algorithms. The machine learning algorithms were either supervised or unsupervised, depending on the approach used. Supervised machine learning algorithms use classifications and regressions, while unsupervised use clustering and associations to determine outliers in the data. Algorithms that were used in the designed models for gait abnormality were: Bayesian algorithm, K-nearest neighbors algorithm (KNN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (RNN-LSTM), isolated forest (IF) algorithm, and one-class support vector machine (OC-SVM) algorithm. Depending on the algorithm that was used and the mythological approach, different accuracy were achieved. The Bayesian algorithm was commonly used in [15, 19, 23, 33] for the assessment of gait abnormalities. RNN-LSTN algorithm was used in three of the studies [20, 36, 37, 40]. Amr Elkholy et al. used unsupervised one-class support vector machine (OC-SVM) and isolated forest algorithms for abnormal detection in [31, 33]. Some other algorithms such as the EDSS and MSWS were also applied in [27]. The algorithms used in the various studies cannot be compared to determine which is more efficient and robust. This is because different methods and data sets were used to achieve the desired accuracy.The general limitation of the summarised studies has to do with the relatively small data set used. Most of the studies did not use a large data set to test the robustness of the trained model except in [21, 22, 25, 29, 33, 34, 44] that used large data sets. Another limitation was some gait parameters were not used in training the models for abnormal gait assessment. Some studies did not also include key joint angles in the trained model. Therefore, some of the models did not give high precision and robustness in assessing gait abnormalities. Also, the use of Kinect v1 has limited capabilities compared to Kinect v2 that has more enhanced 3D skeletal tracking capabilities.


### 4.2. Posture Abnormality or Disorder

From Table4, some body features were measured to determine the posture abnormality of a person. They were the body center mass position and the shoulder position angulation. The head position and the neck angles were also essential to detect postural instability. In [47,49], the center of body mass was used to determine the postural instability. The rapid upper limb assessment (RULA) method was commonly used to assess postural instability in [52,57]. The distance between the neck, shoulder blade, and angles was very essential in determining the abnormal posture of a person. In [52], the height, hips, and shoulder position were measured as well as the shoulder angles. In [53], the knee joints, ankle joints, the lateral joints, and interior joints were computed. The spine angle was also used in [47] to determine the abnormal posture of an individual.Different algorithms were used to determine postural disorder from the summarised studies. The algorithms that were used included pattern recognition neural algorithm, CoVNet model, Berg balance scale (BBS) method, and the RULA technique. These algorithms were used to track the static body position features and key joint angles to determine the postural instability of a person. The accuracy in the detection of posture disorder was not considered because the studies focused only on determining if there was posture abnormality.The limitations of the various summarised studies largely depended on the small data set used in the various studies.


### 4.3. Mathematical Analysis of Gait Abnormality

In Sun Bie et al. [21], the spatial position was used with the associated joints for each subject walking. The extracted joint angle formed were then calculated. The equations used in the joint angle computation were given by the following equation:(1)leni,k=Ji−Jk,θi,k,j=cos−1Ji−Jk∗Jj−JkJi−Jk−Jj−Jk,where J[i] represents the joints, len (i,k) represents the distance between joint i, and kθ(i, k, j) describes the angle formed by joint i, k, and j. Therefore, the angles formed by the left leg and the right leg were calculated by the following equation:(2)Rs=ρθl,θr=Εθl−mθl∗θr−mθrΕθl−mθl∗Εθr−mθr,where θl and θr represents the angles formed on the left and right leg, respectively, of a subject walking and E(∗)represents the expected value of the operation for the simulation. The equations were used in computing the key joint angles to determine gait asymmetry. The angles calculated were the hip angle, knee angle, and the ankle angle. The challenge with this technique is that there may be some difficulties in measuring the inner joint angles of subjects. The gait cycles were computed and given by the following equation:(3)Ts=1n−1∑i=2i=nxi−xi−1∗0.0333,where ∗ is the absolute value from the operation and 0.0333 is the conversion factor from the Kinect sensor.In [37], the gait energy image (GEI) was used based on the Gaussian mixture model (GMM) for each pixel in the simulation. The GEI was the image captured with the Kinect sensor of an individual in a walkway. It can be used to determine the dynamic information of a gait sequence. The gait cycle was then extracted and computed at the point where a normalised autocorrelation from the silhouette image was high in the GEI.(4)CN=∑x,y∑n=0kSx,y,nSx,y,n+N∑x,y∑n=0kSx,y,n2∑x,y∑n=0kx,y,n+N2,where C(N) represents the autocorrelation for N frameshift, and the N value is chosen to empirically represent all the abnormal gait cycles that exist in the tested data sets; K = NTotal–N–1 where NTotal represents the total number of frames sequence. S(x, y, n) indicates the pixel values at a position (x,y) in the silhouette frame n. The GEI was then computed as an average of the normalised and aligned silhouette over the gait cycle in the following equation:(5)Gx,y=1ncycle∑i=1ncycleSix,y,where ncycle represents the frame of the gait cycle while Si is the silhouette frame of x, y pixel coordinates of the image captured. The extracted gait energy image (GEI) and the gait cycle were used to determine gait abnormality from different viewing points based on the colour image sequence. However, this technique does not consider factors that may affect the colour image sequence such as clothes variations.In [32], the Euclidian distance was used to compute the joints and dynamic body parts for a subject to determine gait abnormality. The Euclidian distance is defined in the following equation, where the distance r and s are the shortest distance between the line segment rs:(6)dr,s=ds,r=rx−sx2+ry−sy2+rz−sz2.Hero’s formula was then used to calculate the triangular area of the gait cycle. The area obtained by Hero’s formula is given by the following equation:(7)Area_of_triangle=rx−sx2+ry−sy2+rz−sz2.The step length was then calculated between the right foot from left foot in the Euclidian distance as follows:(8)step_length=∑i=1nA+B,where A and B are defined as the area formed by the joint angles.The areas and angles were formed between the hip right foot and the left foot. A triangle was generated to get the area and angle between the right foot. Therefore, the Euclidean distance to compute for the maximum foot distance lifted from the ground is given by the following equation:(9)Groundclearance=maxright_footy−minright_footy.The limitations in using the Euclidean distance for computation has to do with the multiple dimensions and the sparse nature of data. This presents some variations in trying to measure the gait distances for subjects in a walkway.In [44], asymmetry features were used to detect walking abnormality in a subject. The motion asymmetry between the right body parts and the left body parts of the skeletal data was extracted. The average distance extracted from the skeletal data for a pair of joint angles was then computed. The Euclidean distance used to represent the asymmetry feature was calculated from the following equation:(10)Dijp=∑t=1nxit−xjt2+yit−yjt2+zit−zit2n,where Dρ represents the left and right of the average distance of a subject while xit, yit, and zit represents the 3D coordinates of the joint i of a subject of frame t and n is the number of frames of action sequence. Np is the set of joints for the left and right body parts of an individual. The velocity magnitude feature was computed in the study to detect slow action performed by the subject. The equation used to calculate is as follows:(11)V=∑t=1n−1∑i=1Nxit+1−xit2+yit+1−yit2+zit+1−zit2n−1N.Equation (11) is essential in computing the displacement magnitude for each body joint between two successive frames where N represents the number of joints and n represents the number of frames.In [27], gait assessment of a patient was evaluated by extracting the time series for the knee angles and the gait cycle of dynamic time warping (DTW). The knee DTW distance and the hip were then calculated and averaged to get the mean DTW distance of individual patients. The mean DTW distance for the hip joints and the knee that are denoted by DHP and DKP are defined by the following equations:(12)DKP≔121mpncmc∑j=1mp∑q=1nc∑r=1mcDTWθLKi,t,ϕLKq,r+1mpncmc∑j=1mp∑q=1nc∑r=1mcDTWθRKi,t,ϕRKq,r,(13)DHP≔121mpncmc∑j=1mp∑q=1nc∑r=1mcDTWθLHi,t,ϕLHq,r+1mpncmc∑j=1mp∑q=1nc∑r=1mcDTWθRHi,t,ϕRHq,r.


### 4.4. Equations for Postural Abnormality Assessment

Several researchers have proposed different techniques for the recognition of the human posture and 3D human reconstruction. The posture probability density is used to reconstruct the posture of human beings [60]. It is based on human body measurement that can be used to determine posture. This is used as a density estimator in the following equation:(14)p^x;h,γ=∑n=1Nγnκx−xnh,where K(.) is the kernel, h is the bandwidth of the kernel, and d is the degree of freedom of the data. The probability density estimate is then given by the function where the weights γ in each kernel are based on the reduced set density estimation (RSDE).The RULA-based method is a common technique for posture assessment of an individual. This technique can be used to determine the postural abnormality of an individual. Two techniques are used to calculate the joint angles, which are input from a module score. These techniques use a voxel-based angle estimation in which the RULA score for the upper joints is computed based on their location. The joint angles are computed using vectors that are dependable on the location of each joint with correspondence to the parent joint location. This is given by the angle between two vectors of the parents’ joints and the child’s joints in the following equation:(15)θ=cos−1p1.p2p1.p2.The magnitude of the two vectorsP1 and P2 are calculated by:p1=p1.p1 and p2=p2.p2, where the computed value is then submitted into equation (15). The RULA method is a good technique for posture assessment because it is easy and fast to use. This can be used in the evaluation of posture disorder without the need to conduct any experimental measurements. This technique is, therefore, significant to conduct risks of musculoskeletal disease with regard to the posture of an individual.


## 4.1. Gait Abnormality or Disorder

The purpose of this study is to review the available studies using Microsoft Kinect for the assessment of gait and posture abnormalities. The key features measured included the angles formed by leg swing, speed, and distance of each gait step. These parameters were useful in detecting gait asymmetry in order to distinguish normal from abnormal gait. Some other components from the summarised studies were the algorithms used, the major findings of each study, and limitations.From the reviewed articles, various methods were employed in assessing and detecting gait abnormality. The gait features captured were mainly the step/stride length (m), gait speed (m/s), gait cycle (deg), and step time (s). Some other gait features captured from the studies were angles of knee joints, ankle joints, and hip angles joints. The measured joint angles were used to train the models in detecting gait abnormalities. The measurement of joint angles helped improve the efficiency and robustness of the trained models to detect gait abnormality.They were various algorithms used to train the models for gaits abnormality detection. Some of the algorithms used in Table3 were machine learning algorithms. The machine learning algorithms were either supervised or unsupervised, depending on the approach used. Supervised machine learning algorithms use classifications and regressions, while unsupervised use clustering and associations to determine outliers in the data. Algorithms that were used in the designed models for gait abnormality were: Bayesian algorithm, K-nearest neighbors algorithm (KNN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (RNN-LSTM), isolated forest (IF) algorithm, and one-class support vector machine (OC-SVM) algorithm. Depending on the algorithm that was used and the mythological approach, different accuracy were achieved. The Bayesian algorithm was commonly used in [15, 19, 23, 33] for the assessment of gait abnormalities. RNN-LSTN algorithm was used in three of the studies [20, 36, 37, 40]. Amr Elkholy et al. used unsupervised one-class support vector machine (OC-SVM) and isolated forest algorithms for abnormal detection in [31, 33]. Some other algorithms such as the EDSS and MSWS were also applied in [27]. The algorithms used in the various studies cannot be compared to determine which is more efficient and robust. This is because different methods and data sets were used to achieve the desired accuracy.The general limitation of the summarised studies has to do with the relatively small data set used. Most of the studies did not use a large data set to test the robustness of the trained model except in [21, 22, 25, 29, 33, 34, 44] that used large data sets. Another limitation was some gait parameters were not used in training the models for abnormal gait assessment. Some studies did not also include key joint angles in the trained model. Therefore, some of the models did not give high precision and robustness in assessing gait abnormalities. Also, the use of Kinect v1 has limited capabilities compared to Kinect v2 that has more enhanced 3D skeletal tracking capabilities.


## 4.2. Posture Abnormality or Disorder

From Table4, some body features were measured to determine the posture abnormality of a person. They were the body center mass position and the shoulder position angulation. The head position and the neck angles were also essential to detect postural instability. In [47,49], the center of body mass was used to determine the postural instability. The rapid upper limb assessment (RULA) method was commonly used to assess postural instability in [52,57]. The distance between the neck, shoulder blade, and angles was very essential in determining the abnormal posture of a person. In [52], the height, hips, and shoulder position were measured as well as the shoulder angles. In [53], the knee joints, ankle joints, the lateral joints, and interior joints were computed. The spine angle was also used in [47] to determine the abnormal posture of an individual.Different algorithms were used to determine postural disorder from the summarised studies. The algorithms that were used included pattern recognition neural algorithm, CoVNet model, Berg balance scale (BBS) method, and the RULA technique. These algorithms were used to track the static body position features and key joint angles to determine the postural instability of a person. The accuracy in the detection of posture disorder was not considered because the studies focused only on determining if there was posture abnormality.The limitations of the various summarised studies largely depended on the small data set used in the various studies.


## 4.3. Mathematical Analysis of Gait Abnormality

In Sun Bie et al. [21], the spatial position was used with the associated joints for each subject walking. The extracted joint angle formed were then calculated. The equations used in the joint angle computation were given by the following equation:(1)leni,k=Ji−Jk,θi,k,j=cos−1Ji−Jk∗Jj−JkJi−Jk−Jj−Jk,where J[i] represents the joints, len (i,k) represents the distance between joint i, and kθ(i, k, j) describes the angle formed by joint i, k, and j. Therefore, the angles formed by the left leg and the right leg were calculated by the following equation:(2)Rs=ρθl,θr=Εθl−mθl∗θr−mθrΕθl−mθl∗Εθr−mθr,where θl and θr represents the angles formed on the left and right leg, respectively, of a subject walking and E(∗)represents the expected value of the operation for the simulation. The equations were used in computing the key joint angles to determine gait asymmetry. The angles calculated were the hip angle, knee angle, and the ankle angle. The challenge with this technique is that there may be some difficulties in measuring the inner joint angles of subjects. The gait cycles were computed and given by the following equation:(3)Ts=1n−1∑i=2i=nxi−xi−1∗0.0333,where ∗ is the absolute value from the operation and 0.0333 is the conversion factor from the Kinect sensor.In [37], the gait energy image (GEI) was used based on the Gaussian mixture model (GMM) for each pixel in the simulation. The GEI was the image captured with the Kinect sensor of an individual in a walkway. It can be used to determine the dynamic information of a gait sequence. The gait cycle was then extracted and computed at the point where a normalised autocorrelation from the silhouette image was high in the GEI.(4)CN=∑x,y∑n=0kSx,y,nSx,y,n+N∑x,y∑n=0kSx,y,n2∑x,y∑n=0kx,y,n+N2,where C(N) represents the autocorrelation for N frameshift, and the N value is chosen to empirically represent all the abnormal gait cycles that exist in the tested data sets; K = NTotal–N–1 where NTotal represents the total number of frames sequence. S(x, y, n) indicates the pixel values at a position (x,y) in the silhouette frame n. The GEI was then computed as an average of the normalised and aligned silhouette over the gait cycle in the following equation:(5)Gx,y=1ncycle∑i=1ncycleSix,y,where ncycle represents the frame of the gait cycle while Si is the silhouette frame of x, y pixel coordinates of the image captured. The extracted gait energy image (GEI) and the gait cycle were used to determine gait abnormality from different viewing points based on the colour image sequence. However, this technique does not consider factors that may affect the colour image sequence such as clothes variations.In [32], the Euclidian distance was used to compute the joints and dynamic body parts for a subject to determine gait abnormality. The Euclidian distance is defined in the following equation, where the distance r and s are the shortest distance between the line segment rs:(6)dr,s=ds,r=rx−sx2+ry−sy2+rz−sz2.Hero’s formula was then used to calculate the triangular area of the gait cycle. The area obtained by Hero’s formula is given by the following equation:(7)Area_of_triangle=rx−sx2+ry−sy2+rz−sz2.The step length was then calculated between the right foot from left foot in the Euclidian distance as follows:(8)step_length=∑i=1nA+B,where A and B are defined as the area formed by the joint angles.The areas and angles were formed between the hip right foot and the left foot. A triangle was generated to get the area and angle between the right foot. Therefore, the Euclidean distance to compute for the maximum foot distance lifted from the ground is given by the following equation:(9)Groundclearance=maxright_footy−minright_footy.The limitations in using the Euclidean distance for computation has to do with the multiple dimensions and the sparse nature of data. This presents some variations in trying to measure the gait distances for subjects in a walkway.In [44], asymmetry features were used to detect walking abnormality in a subject. The motion asymmetry between the right body parts and the left body parts of the skeletal data was extracted. The average distance extracted from the skeletal data for a pair of joint angles was then computed. The Euclidean distance used to represent the asymmetry feature was calculated from the following equation:(10)Dijp=∑t=1nxit−xjt2+yit−yjt2+zit−zit2n,where Dρ represents the left and right of the average distance of a subject while xit, yit, and zit represents the 3D coordinates of the joint i of a subject of frame t and n is the number of frames of action sequence. Np is the set of joints for the left and right body parts of an individual. The velocity magnitude feature was computed in the study to detect slow action performed by the subject. The equation used to calculate is as follows:(11)V=∑t=1n−1∑i=1Nxit+1−xit2+yit+1−yit2+zit+1−zit2n−1N.Equation (11) is essential in computing the displacement magnitude for each body joint between two successive frames where N represents the number of joints and n represents the number of frames.In [27], gait assessment of a patient was evaluated by extracting the time series for the knee angles and the gait cycle of dynamic time warping (DTW). The knee DTW distance and the hip were then calculated and averaged to get the mean DTW distance of individual patients. The mean DTW distance for the hip joints and the knee that are denoted by DHP and DKP are defined by the following equations:(12)DKP≔121mpncmc∑j=1mp∑q=1nc∑r=1mcDTWθLKi,t,ϕLKq,r+1mpncmc∑j=1mp∑q=1nc∑r=1mcDTWθRKi,t,ϕRKq,r,(13)DHP≔121mpncmc∑j=1mp∑q=1nc∑r=1mcDTWθLHi,t,ϕLHq,r+1mpncmc∑j=1mp∑q=1nc∑r=1mcDTWθRHi,t,ϕRHq,r.


## 4.4. Equations for Postural Abnormality Assessment

Several researchers have proposed different techniques for the recognition of the human posture and 3D human reconstruction. The posture probability density is used to reconstruct the posture of human beings [60]. It is based on human body measurement that can be used to determine posture. This is used as a density estimator in the following equation:(14)p^x;h,γ=∑n=1Nγnκx−xnh,where K(.) is the kernel, h is the bandwidth of the kernel, and d is the degree of freedom of the data. The probability density estimate is then given by the function where the weights γ in each kernel are based on the reduced set density estimation (RSDE).The RULA-based method is a common technique for posture assessment of an individual. This technique can be used to determine the postural abnormality of an individual. Two techniques are used to calculate the joint angles, which are input from a module score. These techniques use a voxel-based angle estimation in which the RULA score for the upper joints is computed based on their location. The joint angles are computed using vectors that are dependable on the location of each joint with correspondence to the parent joint location. This is given by the angle between two vectors of the parents’ joints and the child’s joints in the following equation:(15)θ=cos−1p1.p2p1.p2.The magnitude of the two vectorsP1 and P2 are calculated by:p1=p1.p1 and p2=p2.p2, where the computed value is then submitted into equation (15). The RULA method is a good technique for posture assessment because it is easy and fast to use. This can be used in the evaluation of posture disorder without the need to conduct any experimental measurements. This technique is, therefore, significant to conduct risks of musculoskeletal disease with regard to the posture of an individual.


## 5. Conclusion

In this study, we presented Microsoft Kinect as a noncontact tool for the assessment of gait abnormality and posture disorder. While there are several studies on gait recognition, only a few have dealt with the assessment of gait and posture abnormalities. Early detection of gaits and posture abnormalities plays a significant role for clinicians to provide corrective rehabilitation measures. Even though this is a comprehensive study, there may be some articles that are not included.In our study, we presented 26 studies for gait abnormality assessment and 13 articles for posture disorder. The summarised studies differ by the methodology used, the gait features extracted, and the analytical tools used to process the skeletal data. Different algorithms were applied in the summarised studies, and some of them made use of machine learning algorithms. The results showed what has been done so far in the area of gait and posture abnormality assessment.From our analysis, Kinect sensors have a high success rate of approximately 87% in abnormalities assessment. It has an accuracy ranging between 83% and 98.1% from the summarised articles for gait abnormality. This is quite acceptable in the clinical settings for the purposes of diagnosis of diseases associated with gait and posture disorders. Although Microsoft has stopped the release of Kinect sensors, it is still an important tool for diagnostic purposes. It can be concluded that Kinect sensor is an essential monitoring tool for use in medical diagnostics and can also help track the progress of patients who are undergoing rehabilitation.



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*Source: 4360122-2021-11-01.xml*