Background

Knee osteoarthritis (KOA) is a degenerative joint disease with a global prevalence of 16.0% in individuals aged 15 and over and 22.9% in individuals aged 40 and over, incidence of 203 per 10,000 person per year in individuals aged 20 and over, and the ratios of prevalence and incidence in females and males of 1.69 and 1.39, respectively [1]. A prevalence of 28.7% was found in Indian population with higher prevalence in villages (31.1%) and big cities (33.1%) as compared to towns (17.1%) and small cities (17.2%) [2]. The degenerative changes associated with KOA lead to biomechanical changes which eventually lead to an alteration in the gait. These patients attempt to unload the affected joint while walking by developing altered gait habits and hence have more swing phase than stance phase. The gait of patients with KOA is often distinguished by a higher knee adduction moment, a medial joint load marker, and a recognized risk factor for arthritis progression [3]. The altered gait pattern leads to changes in the kinematics, kinetics, and spatiotemporal gait parameters [4] and linked with KOA growth and progression. It is thought that abnormal gait can be a cause and effect of KOA and these abnormal gait patterns can be used as a diagnostic index [5]. Therefore, gait analysis becomes an essential tool as it provides better understanding of biomechanical abnormalities related to development and progression of KOA. It also leads to a better planning and designing of the therapeutic program [3] for the patients as gait correction becomes one of the primary goal.

Different methods for assessing gait are present in the literature. These methods vary from basic visual observation to video images to more computer-based 3D approaches that are more comprehensive. Technological developments have contributed to the rise of wearable sensors, and hence it is possible to capture and analyze gait beyond traditional gait laboratories with these sensors [6]. Although gait assessment is essential in assessment and management of gait in KOA, there are marked discrepancies in the methods used by researchers to analyze gait. Therefore, this literature review provides an in-depth critical analysis of the current methodologies for gait analysis in KOA.

Method

A literature review search was conducted (till 30 September 2020) on the database of PubMed, Science Direct, Google Scholar, and Web of Science using keywords: gait analysis technologies, knee osteoarthritis, wearable sensors, hybrid system, optoelectronic system. This narrative review includes literature data from randomized controlled trials and from review articles, summarizing the studies which include gait assessment in KOA patients. This article discusses common gait technologies used for KOA and the significance of this specific field of research, focusing on KOA gait. In this article, available gait technologies are briefly introduced by reflecting on the benefits and drawbacks of the technologies.

Observational Gait Assessment (OGA)

It is the most common method used to assess gait in clinical practice. The advantage of this method is that it does not require any equipment which can be an important consideration for clinical practice. But, this method is inadequate in providing quantitative data which affects its accuracy and reproducibility of the measurements. Thus, minor gait alterations could be undetected and the research appropriate data is unattainable. In comparing 3D gait analysis and OGA in KOA, Taş et al. [4] recorded the lowest rate of agreement in both validity (r = 0.06, p > 0.05) and inter- (ICC − 0.12–0.06) and intra-observer (ICC 0.30–0.45) OGA reliability. Various factors reduce the validity and reliability of OGA such as unclear gait disorders, joint stiffness which lead to inconsistent gait pattern, and high BMI [4]. All these limitations discourage the use of this method for scientific research purposes. With the advancement in technology, various gait analysis methods which facilitate effective diagnosis and treatment of KOA have been developed and provide accurate quantitative based on different gait parameters.

Common Technologies Used in Gait Analysis of KOA

Current gait technologies used for gait assessment of KOA can be discussed under three distinct types: vision-based, sensor-based, and hybrid/combination [7].

Vision-Based Gait Assessment

This method involves the use of optoelectronic system. It is a type of optical sensor that uses digital cameras to detect human movement and thus estimate motion parameters and orientation more accurately [7]. Vision-based modality is classified into two categories based on application of markers: marker-based and markerless. In marker-based modality, several active or passive retro-reflective markers are attached on the body landmarks that specify joint angles. To detect the position of indicated body landmarks, video-based optoelectronic devices such as VICON [8] and Qualisys [9] are then used. A research was carried out by Ishikawa et al. [5] to determine the angle of elevation during gait in KOA and healthy subjects. A plug-in-gait marker collection (eight markers) and nine VICON cameras were used. Results revealed that planar law was applicable to patients and obtained improved precision 0.69 ± 0.14, (curved area) AUC 0.69 ± 0.767, accuracy = 0.84 ± 0.23, recall 0.57 ± 0.26. This study shows that gait motion evaluation using elevation angle offers a valid diagnosis metric for KOA with a single index [5].

Markerless modality uses only single camera such as Kinect V2 [10] and no markers are attached on patient’s body. For effective and precise gait analysis of KOA patients, Cui et al. [11] used markerless modality using a single Kinect sensor consisting RGB-D camera to capture the depth details of patient body joints. They suggested that applying support vector machine—a machine learning method for classification—results in Kinect’s high efficiency in KOA diagnosis with a 97% accuracy rate [11].

Optoelectronic system produces tremendous amount of gait data. Analysis and reduction of this data is a barrier to clinical use of gait information. Deluzio et al. [12] conducted a gait study in KOA and healthy subjects to assess difference in gait pattern using optoelectronic system. Principal component analysis was used for gait data reduction and explanation. The main purpose of principal component analysis is to summarize the most important information in the gait data. The principal component waveform analysis technique used in this study identified gait pattern differences in the knee flexion angle, the knee adduction moment, and the knee flexion moment [12]. Similarly, Federolf et al. [13] identified systematic gait differences between healthy and patients with medial KOA using principal component analysis. Machine learning approaches such as principal component analysis and support vector machine can provide insight into complex relationships of biomechanical gait variables, as compared to multiple univariate analysis methods [14]. Optoelectronic system provides robust and precise acquisition of physical movements over virtual modeling [15]. But this system needs special setup for experiment, expensive to conduct, and need to combine with other methods for effective gait assessment.

Sensor-Based Gait Assessment

Sensor-based devices for gait assessments are classified into two categories: non-wearable sensors and wearable sensors [16]. Table 1 provides a description of these sensors.

Table 1 Description of non-wearable and wearable sensors
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Non-wearable Sensors

The patients walk on a clearly marked walkway on which sensors are embedded and gait data is captured [16]. These sensors are useful for gait assessment as they calculate forces derived from foot-to-ground contact while the patients walk on them [7] which is especially helpful in identifying faulty contact forces. It includes force plates, electronic and pressure mats, and instrumented treadmill. As the patients walk on them, these methods quantify forces and translate them into electrical signals for the measurement of the center of pressure and ground reaction forces [16]. The floor sensors have no direct attachment on subject body. However, they are suitable only for laboratory and need to combine with limb kinematics for gait analysis [15]. Two Kistler force plates on a 6-m long walkway were used by Kotti et al. [17] to examine ground reaction force by using random forest method and assess the efficacy of rule-based approach in 47 KOA and healthy subjects. Using the random forest regression learning process, a fivefold cross-validation precision of 72.61% ± 4.24% was achieved. The study concluded that random forests are ideal for evaluating ground reaction forces to differentiate patients with KOA from healthy ones [17].

GAITRite® system [18] is a type of foot sensor used to assess spatiotemporal gait variables. It is a 5.4-m rubber electronic mat with embedded pressure sensors. It is a portable, no attachments on subject, and easy-to-use device that exhibits excellent test–retest reliability in gait assessment of older individuals (ICC ranging from 0.82 to 0.91, depending on the evaluated parameter) [19]. But it needs to combine with limb kinematics and usable for laboratory only [15]. Peixoto et al. [20] utilized GAITRite® system on which KOA patients walked at self-selected speed. They observed that older women with bilateral KOA walked with reduced speed, cadence, and step length, but have symmetrical step length and single support phase between lower limbs [20].

The instrumented treadmill consists of a treadmill ergometer with an integrated pressure sensor mat with force sensors and analysis software. The system measures the dynamic pressure distribution under the feet while walking on the treadmill. Spatiotemporal gait characteristics are computed automatically from the pressure data within the software. Using instrumented treadmill with tandem piezo-electric force plates, Wiik et al. [21] assessed gait patterns and ground reaction force symmetry in KOA patients at higher walking speed. They showed that KOA patients walk more slowly and asymmetrically, with wider base of support and a shorter step length. They also showed less symmetrical push-off force and impulse in KOA suggesting a weakness during the terminal stance phase as a factor causing slower walking speeds [21].

Wearable Sensors

Wearable sensors are placed on patients body and results are analyzed even outside the laboratory [16]. They are non-invasive, low cost, small-sized, low weight, power-efficient, and wirelessly connected [22]. Wearable sensors are of different kinds based on their function. They include inertial sensors, electromyography (EMG), electrogoniometers, pressure sensors, and ultrasonic sensors.

Inertial Sensors.

They consist of a combination of accelerometers and gyroscopes to measure angular velocity, acceleration, direction, and gravitational forces [16]. They are inexpensive, completely portable devices that can be used in almost any environment and allow 3D measurement to be made possible by measuring triaxial data. In capturing kinematic data, accelerometers and gyroscopes are as effective as the 3D motion capture device; as they are reliable, repeatable, and calculate metrics at similar accuracy to motion capture system [23, 24]. The drawback of using this device is the artifacts of skin movement that can impact the readings [15]. In KOA patients following complete knee arthroplasty, Tereso et al. [25] investigated spatiotemporal, posture, and fall-related consequences by using assistive devices, using two 3-axis accelerometers, one was attached to the operated leg ankle to measure spatiotemporal parameters, and the other at the sacrum (trunk) to measure posture and fall risk-related parameters. This study concluded that assistive devices should be prescribed depending on the state of recovery of the patient and demonstrated that standard walker is good to give stability while rollator with forearm supports provide a gait pattern closer to a natural gait [25]. A single 3D inertial sensor was used by Bolink et al. [26] to assess the spatiotemporal and kinetic gait characteristics of KOA patients. The findings showed the potential of inertial sensors in KOA and reported that KOA patients had lower walking speed, knee flexion, and more trunk lean [26].

Accelerometers and gyroscopes are fitted with the Intelligent Device for Energy Expenditure and Activity (IDEEA) [27], thus making them a valuable, time-based device composed of sensors and recorders for tracking movement and calculating gait parameters with greater data storage. The accuracy and reliability of IDEEA3 measurements for cadence, gait phase, and velocity step length and step counts in KOA patients were assessed by Sun et al. [6] and found that it is an accurate instrument for calculating gait parameters in KOA.

Accelerometer provides the opportunity to take advantage of advanced analytical methods such as autocorrelation analysis, which can be used to extract discrete parameters such as the stride time and step time, in addition to using the entire acceleration waveform to determine the regularity and symmetry of the gait cycle [28]. These findings suggest that waist-mounted accelerometry and autocorrelation analysis could be used to conduct clinical assessments of gait abnormalities in individuals with KOA.

Electromyography

EMG is a tool which makes it easy to study muscle functions. The EMG signal can be measured either by surface electrodes or by needle electrodes. The signal is amplified, conditioned, and recorded afterwards [16]. But, it needs to combine with other system response for effective gait analysis [15]. Various studies confirmed that EMG is a useful assessment method in many musculoskeletal problems affecting gait. Hubley-Kozey et al. [29] conducted a gait assessment using EMG to determine activation of major muscles crossing the knee joint during ambulation in KOA. The EMG data were entered into a pattern recognition procedure that captured both the amplitude and shape characteristics of EMG waveforms. KOA patients demonstrated a high degree of agonist/antagonist co-activity around the knee joint during ambulation thus leading to a slower speed than healthy control [29]. Pattern recognition procedure provides a novel approach to quantify synergistic co-activity. With the advancement of wireless technologies and its application to sensors, EMG has become a very accurate and wearable gait analysis tool [30].

Electrogoniometry

Electrogoniometers are widely used to measure joint angles of the body, such as the ankle, knee, and hip. Two kinds of electrogoniometers, potentiometer and strain gauges (Fig. 1), are commonly used. They are cheap, provide immediate output signals, and do not need complicated algorithms for processing. However, they are cumbersome to use, provide only single plane movement and limited gait parameters, and are often difficult to match for joints with more than one degree of freedom [15]. Tarniţǎ et al. [31] compared KOA patients with healthy subjects using a treadmill and electrogoniometers for each leg to determine knee range of motion and amplitude of flexion–extension moments. The placement of electrogoniometer is shown in Fig. 2. Study concluded that KOA patients had less range of motion during the gait cycle than the healthy subjects and a large difference between the amplitude of knee flexion during 25–50% of gait cycle phase and 65–80% of gait cycle phase could be due to the walking speed [31].

Fig. 1
figure 1

Adapted from Muro-de-la-Herran A, García-Zapirain B, Méndez-Zorrilla A. 2014 [16]

Flexible electrogoniometer.

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Fig. 2
figure 2

Adapted from Tarniţǎ D, Catanǎ M, Tarniţǎ DN. 2013 [31]

Placement of electrogoniometers (strain gauge).

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Pressure Sensors

Pressure sensors measure the forces applied on the sensor (Fig. 3). Pressure sensor devices can be used everywhere because of active attachment of sensors with shoes but for rough surfaces and moving up-down stairs, they are less efficient, and to measure the ground reaction force and center of pressure, these devices must be combined with limb kinematic data [15]. Muñoz-Organero et al. [32] examined 14 KOA patients with healthy subjects to determine the correlation between mild knee pain and plantar loading using a smart insole equipped with pressure sensors. All subjects wore the insole and walked 10 m. Data was recorded from eight sites on sole of foot using piezoresistive sensors using kinematrix laptop application. Study provides evidence that patients with mild knee pain delay the transition from heel to midfoot loading and move maximum pressure time in midfoot region toward the maximum pressure time in forefoot [32].

Fig. 3
figure 3

Adapted from Muro-de-la-Herran A, García-Zapirain B, Méndez-Zorrilla A. 2014 [16]

Instrumented shoe. a Inertial measurement unit; b flexible goniometer; and c pressure sensors inside the insole.

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Foot switches are pressure sensors commonly used to assess spatiotemporal parameters of gait. Spinoso et al. [33] used foot switches in KOA patients and located foot switches bilaterally at the calcaneus and hallux base to determine gait phases (Fig. 4). The study concluded that in contrast to healthy controls, KOA patients walked with slow pace, longer support time, and a longer step time reduction in swing time.

Fig. 4
figure 4

Adapted from Spinoso DH, Bellei NC, Marques NR, Navega MT. 2018 [33]

Foot Switch (Noraxon®)—a pressure sensors positioning for gait assessment.

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Ultrasonic Sensors

To measure spatiotemporal parameters, ultrasonic sensors are used. These sensors are used to indicate heel contact and can be used on uneven or bumpy walking surface and also for ascending-descending stairs; however, they are not accurately providing information because of noise [15]. A computerized ultrasound-based motion analysis system (Zebris CMS-HS—a triplet of UV sensors) (Fig. 5) was used by Kiss [34] to examine the impact of speed with different grades of KOA on the gait. This study found that variability in gait parameters increased when the walking speed varied from the self-selected speed and this variation is more prominent in severe grades of KOA [34].

Fig. 5
figure 5

Adapted from Kiss RM. 2011 [34]

Gait assessment using ultrasonic sensor.

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Hybrid System

This system incorporates both vision-based and sensor-based technologies (Fig. 6) to measure gait effectively and accurately [7]. It provides quantitative information about kinematics, spatiotemporal, and kinetics data [35]. This system consists of motion sensors mounted to the body and force plates under the foot that provide three-dimensional data on forces and moments. The accelerometer and gyroscope sensor can also be combined with force plates [15]. Therefore, for successful gait analysis, these instruments are used in many clinical studies. The maximum number of KOA gait assessment studies was carried out on this modality, reflecting its usefulness in the evaluation of KOA gait. But there is a lack of appropriate reporting protocol to use hybrid technology to assess gait in KOA. Different studies utilized different device combinations, different kinematic data collection frequencies (50 to 200 HZ), kinetic data collection frequencies (50 to 2000 HZ), number of sensors, force plates, and number of reflective markers, which are included in the analysis of KOA gait study. Table 2 provides the list of gait analysis studies done in KOA using hybrid system along with the key findings. The integration of various modalities offers additional quantitative data of subjects that will enable a more precise measure of KOA gait. Most of the studies use these modalities to enable more productive KOA gait evaluation but are constrained by wide space and heavy setup requirements.

Fig. 6
figure 6

Adapted from BTS GAITLAB | Integrated Gait Analysis Systems | BTS Bioengineering. [36]

BTS Gait Lab configuration. (1) infrared video cameras; (2) inertial sensor; (3) ground reaction force measurement walkway; (4) wireless EMG; (5) workstation; (6) video-recording system; (7) TV screen; (8) control station.

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Table 2 Gait dataset used in various researches using hybrid technology
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Discussion

The aim of this review was to provide a description, covering both qualitative and quantitative approaches, of the technologies and methods used for gait analysis in KOA. An OGA in KOA is the most common approach used to evaluate gait, as no equipment is required and it is quick and easy. 3D gait analysis was contrasted with OGA and found that it only offers qualitative data and has low validity and reliability that compromises its accuracy. Both these limitations preclude the use of this method for research purposes. Different gait analysis techniques have evolved with the advancement of technology, providing precise quantitative data centered on various gait parameters that promote successful diagnosis and treatment of KOA.

The utilization of automated systems utilizing vision-based, sensor-based, and hybrid gait analysis modalities has received more attention in the field of KOA diagnosis. In a recent study, Derek et al. [50] used retro-reflective markers in conjunction with an instrumented gait treadmill to examine the variations in gait characteristics between KOA and healthy patients. A total of 94–58% accuracy was effectively attained by the application of inverse dynamics. The hybrid modality’s great potential for KOA has drawn more researchers to this field. Leading the effort, Kotti et al. [17] achieved great accuracy in analyzing KOA gait just utilizing sensor-based modality. Ishikawa et al.’s amazing work [5] employing a model-based method was directed toward vision-based KOA recognition. The potential of planar law to quantify differences in gait was demonstrated by their research. Like thus, Cui et al.’s [1] use of the Kinect sensor for KOA gait collection created new opportunities for vision-based model-free modalities while achieving 97% accuracy.

Data analysis shows that although vision-based KOA diagnosis is very accurate and economical, it has some limitations, including the need for huge spaces, highly precise cameras, and overlapping. While sensor-based modality works well, it is limited by other aspects including cost, power and time consumption, and wearability [7]. It should not be stated that one is better than the other among the modalities based on wearable and non-wearable sensors, since each has different features that make it more appropriate for certain kinds of study. In laboratories or controlled environments, non-wearable sensors are used that separate the sample from external influences that might influence the measurements, allowing for a more monitored interpretation of the parameters being tested. The key concern with non-wearable sensors is that they require a very pricey laboratory configuration. Another issue is their small size, so the subject must walk on a mat for a long time to get relevant evidence, and the subject must therefore take care to properly position his/her feet to get an impression of the whole step. This will change the way patients normally walk, impacting the measurements’ repeatability. Another downside to non-wearable sensors is that, during daily activities, gait cannot be assessed.

In contrast to the limitations of non-wearable sensors, wearable sensors can be used by positioning them on various parts of the body as small sensors and using wireless communication technologies such as Bluetooth to measure gait outside the laboratory. However, these instruments, such as pressure sensors and accelerometers and gyroscopes, can be used with in-lab research to provide cheaper gait analysis solutions. In recent research, the inertial sensor is the most widely used wearable sensor and it is considered to be as effective and precise in gathering kinematic data as 3D gait analysis [23, 24]. Wearable sensors, however, have some limitations, such as complicated analytical methods, the issue of ambient noise, and the need to position them on the body of the participant, which may be unpleasant or intrusive.

The literature thus clearly illustrates the potential for a successful KOA gait assessment with a hybrid gait assessment method. This technology incorporates both vision-based and sensor-based technologies for precise gait measurement. More number of gait variables, such as kinematic, kinetic, and spatiotemporal variables, will thus be measured simultaneously by using hybrid technologies. Although hybrid technologies provide more reliable and accurate gait data, but it is limited by large space and heavy setup requirements. Approximately 70% of research articles on the aforementioned modalities published between 2000 and 2018 that were part of the survey focused on hybrid modes for KOA studies. Force sensors, for example, are the most often used sensor type because they can immediately record gait information. Furthermore, using sensor- and vision-based modalities separately results in good accuracy measures, according to recent research findings. Nonetheless, combining sensor-based and vision-based modalities offers a distinct advantage over using them separately. Improved performance accuracy is mostly owing to increase efficiency in obtaining big and meaningful KOA gait data. Thus, it is evident from the literature that combined factors have the potential to improve KOA diagnosis.

Conclusion and Future Scope

The quantitative methods, categorized as vision, sensor-based, and hybrid technologies, have features that make them powerful and competitive for various types of requirements. Among these technologies, hybrid technology seems to be the most reliable and accurate because it can assess all aspects of gait assessment. Previous studies focused only on medial knee compartment and few severity levels. Future studies should be done to develop a KOA gait dataset, consider all severity levels and all compartment KOA. Research should be carried out with each examination to assess the most suitable sensor sites. Future experiments should also focus on developing technologies to promote greater autonomy at the workplaces and large periods of energy resources to perform studies over lengthy periods of time.