Motion Sequences Research Articles

Acoustic Voice Analysis as a Useful Tool to Discriminate Different ALS Phenotypes.

Approximately 80-96% of people with amyotrophic lateral sclerosis (ALS) become unable to speak during the disease progression. Assessing upper and lower motor neuron impairment in bulbar regions of ALS patients remains challenging, particularly in distinguishing spastic and flaccid dysarthria. This study aimed to evaluate acoustic voice parameters as useful biomarkers to discriminate ALS clinical phenotypes. Triangular vowel space area (tVSA), alternating motion rates (AMRs), and sequential motion rates (SMRs) were analyzed in 36 ALS patients and 20 sex/age-matched healthy controls (HCs). tVSA, AMR, and SMR values significantly differed between ALS and HCs, and between ALS with prevalent upper (pUMN) and lower motor neuron (pLMN) impairment. tVSA showed higher accuracy in discriminating pUMN from pLMN patients. AMR and SMR were significantly lower in patients with bulbar onset than those with spinal onset, both with and without bulbar symptoms. Furthermore, these values were also lower in patients with spinal onset associated with bulbar symptoms than in those with spinal onset alone. Additionally, AMR and SMR values correlated with the degree of dysphagia. Acoustic voice analysis may be considered a useful prognostic tool to differentiate spastic and flaccid dysarthria and to assess the degree of bulbar involvement in ALS.

Deep Learning in Sign Language Recognition: A Hybrid Approach for the Recognition of Static and Dynamic Signs

A speech impairment limits a person’s capacity for oral and auditory communication. A great improvement in communication between the deaf and the general public would be represented by a real-time sign language detector. This work proposes a deep learning-based algorithm that can identify words from a person’s gestures and detect them. There have been many studies on this topic, but the development of static and dynamic sign language recognition models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers’ speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. For the accurate and effective recognition of signs, this study uses two different deep learning-based approaches. We create a real-time American Sign Language detector using the skeleton model, which reliably categorizes continuous signs in sign language in most cases using a deep learning approach. In the second deep learning approach, we create a sign language detector for static signs using YOLOv6. This application is very helpful for sign language users and learners to practice sign language in real time. After training both algorithms separately for static and continuous signs, we create a single algorithm using a hybrid approach. The proposed model, consisting of LSTM with MediaPipe holistic landmarks, achieves around 92% accuracy for different continuous signs, and the YOLOv6 model achieves 96% accuracy over different static signs. Throughout this study, we determine which approach is best for sequential movement detection and for the classification of different signs according to sign language and shows remarkable accuracy in real time.

Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces.

In recent years, motor imagery-based brain-computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement. Ten subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left→Left (LL), Right→Right (RR), Left→Right (LR), and Right→Left (RL)] using the left or right index finger at about 1 s intervals under an auditory prompt of 1 Hz. The movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm. As a result, both the MRCP and ERD features showed the specific temporal-spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71% for LL-versus-RR and LR-versus-RL, respectively. This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs.

Explainable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons

Hand drawing, which requires multiple neural systems for planning and controlling sequential movements, is a useful cognitive test for older adults. However, the conventional visual assessment of these drawings only captures limited attributes and overlooks subtle details that could help track cognitive states. Here, we utilized a deep-learning model, PentaMind, to examine cognition-related features from hand-drawn images of intersecting pentagons. PentaMind, trained on 13,777 images from 3111 participants in three aging cohorts, explained 23.3% of the variance in the global cognitive scores, 1.92 times more than the conventional rating. This accuracy improvement was due to capturing additional drawing features associated with motor impairments and cerebrovascular pathologies. By systematically modifying the input images, we discovered several important drawing attributes for cognition, including line waviness. Our results demonstrate that deep learning models can extract novel drawing metrics to improve the assessment and monitoring of cognitive decline and dementia in older adults.

V2V‐enabled cooperative adaptive cruise control strategy for improving driving safety and travel efficiency of semi‐automated vehicle fleet

AbstractAlthough highly automated vehicle fleets have committed a safe and efficient traffic flow, the next few years are likely the human‐driven vehicles (HDV) and intelligent vehicles (IV) mixed in large‐scale traffic network, where the interaction of HDVs with IVs will become more frequent. In this study, the authors investigate the traffic flow performance in a widespread of V2V environment deployed for all types of HDVs and IVs, levelled from L0 to L5. Based on this assumption, the vehicle movements and operations are collected and assembled into a V2X BSM dataset, and to any vehicle in fleet, the sequential movements and driving behaviours of the vehicle ahead can be predicted in the short term and considered as the input to cooperative adaptive cruise control (CACC) model of the controlled vehicle. The predicted motion of the vehicle ahead is applied to rolling optimization process of a given MPC framework, which calculates appropriate longitudinal operation for each car‐following vehicle and keeps optimal equilibrium state of the vehicle fleets in heterogeneous traffic flow. The experimental results showed that when the front vehicle generates random acceleration/deceleration in saturated and over saturated traffic state, in the car‐following case, the improved CACC controller maintains average car‐following distance at 20–40 m, the acceleration variation in the range [−0.1, 0.25], the gap error changed within [−10, 13], and controls the relative speed variation from −0.25 to 0.3 m/s, compared to an average headway of 20–140 m, acceleration variation in the range [−1.6, 0.65], gap error changed within [−21, 30], and a relative speed variation from −1.75 to 4.1 m/s by the traditional CACC controller. In the car‐in and car‐out cases, the vehicle controlled by the improved CACC controller had a travel displacement of 4050 m, a speed fluctuation interval of [18.7, 27.7], and an acceleration variation range of [−0.15, 0.2], while the vehicle controlled by the traditional MPC controller had a travel displacement of 3800 m, a speed fluctuation interval of [13.8,27.7] and an acceleration variation range of [−2.9, 0.6]. The improved CACC model can adapt more accurately and quickly to the maneouvre of vehicle ahead in V2V environment, which can effectively improve the capacity of heterogeneous traffic flow and the safety and comfort of driving.

GazePlotter – tool for eye movement sequences visualization

GazePlotter – tool for eye movement sequences visualization

Kinematical Approach to the Drilling Mechanisms for n-Sided Polygonal Holes

Abstract The present analysis addresses the generation of regular polygonal paths with any number of sides, odd or even, by use of special rotors revolving with uniquely prescribed motions inside fixed polygonal holes of guidance, which may be coincident or similar to the paths to be traced. The whole rotation motion of the rotor is considered composed of a sequence of Cardan motions so that the active profile of contact is formed by a polycentric sequence of circular arcs. It is shown and proved that the guidance polygon and the target polygon may coincide in the hypothesis of an odd number n of sides, whereas the guidance profile must be larger than the one to be generated for n even, in which case two equal concentric polygons rotated of π/n between each other may be simultaneously generated by two particular points of the rotors. These devices are very useful to drill blind polygonal holes, providing the rotors of proper cutting tools and driving them by constant-speed motors through homokinetic joints.

Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure.

Synthesizing human motion with a global structure, such as a choreography, is a challenging task. Existing methods tend to concentrate on local smooth pose transitions and neglect the global context or the theme of the motion. In this work, we present a music-driven motion synthesis framework that generates long-term sequences of human motions which are synchronized with the input beats, and jointly form a global structure that respects a specific dance genre. In addition, our framework enables generation of diverse motions that are controlled by the content of the music, and not only by the beat. Our music-driven dance synthesis framework is a hierarchical system that consists of three levels: pose, motif, and choreography. The pose level consists of an LSTM component that generates temporally coherent sequences of poses. The motif level guides sets of consecutive poses to form a movement that belongs to a specific distribution using a novel motion perceptual-loss. And the choreography level selects the order of the performed movements and drives the system to follow the global structure of a dance genre. Our results demonstrate the effectiveness of our music-driven framework to generate natural and consistent movements on various dance types, having control over the content of the synthesized motions, and respecting the overall structure of the dance.

Potential and motion design of droplets in a wetting-programable nanoporous surface

In this study, we design a wetting-programable method to create a surface channel for droplets. Superhydrophilic channels can be designed and erased reversibly by selectively masking the wetting and prewetting process on superhydrophobic silicone surfaces with microgroove and nanoporous structures fabricated using a micromachining technique. We found that droplets in this flow channel present a complex wetting behavior. The surface that undergoes infiltration by wetting and prewetting transitions from being a superhydrophobic surface to becoming a superhydrophilic surface. To understand the dynamics of droplet exploration, we measured the spreading distance of the droplet’s leading edge and the spontaneous capillary flow (SCF). The final spreading length of the droplet’s leading edge was also recorded. We found that increasing the prewetting volume enhances the final spreading distance of the droplet’s leading edge and the average SCF speed. Prewetting minimizes the apparent contact angle and pinning, promoting superhydrophilicity and increasing the droplet mobility. To further enhance our control over liquid transport, we incorporated vertical vibration as an active approach to regulate the programmed transport of droplets. We observed a substantial extension in the final spreading distance of the droplet’s leading edge. This extension was attributed to the effective overcoming of the pinning phenomena caused by the application of vertical vibration. With the above-mentioned methods, we achieve guided motion and multi-droplet sequence mixing of droplets with the designed channel shapes. Our results suggest how the wetting behavior dynamics of a multi-level structural surface are affected by prewetting liquid and vertical vibration. This provides insight into the wetting mechanism on porous superhydrophobic/superhydrophilic material.

Hierarchical Motion Planning and Tracking for Autonomous Vehicles Using Global Heuristic Based Potential Field and Reinforcement Learning Based Predictive Control

The autonomous vehicle is widely applied in various ground operations, in which motion planning and tracking control are becoming the key technologies to achieve autonomous driving. In order to further improve the performance of motion planning and tracking control, an efficient hierarchical framework containing motion planning and tracking control for the autonomous vehicles is constructed in this paper. Firstly, the problems of planning and control are modeled and formulated for the autonomous vehicle. Then, the logical structure of the hierarchical framework is described in detail, which contains several algorithmic improvements and logical associations. The global heuristic planning based artificial potential field method is developed to generate the real-time optimal motion sequence, and the prioritized Q-learning based forward predictive control method is proposed to further optimize the effectiveness of tracking control. The hierarchical framework is evaluated and validated by the numerical simulation, virtual driving environment simulation and real-world scenario. The results show that both the motion planning layer and the tracking control layer of the hierarchical framework perform better than other previous methods. Finally, the adaptability of the proposed framework is verified by applying another driving scenario. Furthermore, the hierarchical framework also has the ability for the real-time application.

Effect of lower body, core and upper body kinematic chain exercise protocol on throwing performance among university shot put athletes: A pilot study

A coordinated sequence of movements is required to generate maximum power and velocity in shot put. Kinematic chains emphasize the interactions between various body segments during a movement. They suggest that force production and transfer are optimized by coordinating multiple joints and muscle groups. In previous research, the kinematic chain has been attributed to shot put performance. Few studies have examined the effects of a comprehensive kinematic chain exercise protocol on throwing performance among shot put athletes, particularly at universities. Pilot study investigating lower body, core, and upper body kinematic chain exercise protocol on university shot put athletes' throwing performance. A total of twenty-four young athletes specializing in shotput, with an average age of 19.87 years and a standard deviation of 1.31 years, were divided into two groups, namely the experimental group and the control group, using a random assignment method, the experimental group, consisting of 12 participants, underwent an 8-week kinematic chain training program alongside their regular training sessions. On the other hand, the control group, also consisting of 12 participants, only participated in their regular training sessions without any additional intervention. Pre- and post-training assessments were conducted to measure shotput throwing performance, preference for throwing style, and the participants' satisfaction with the exercise protocol, using a questionnaire. The athletes who took part in the kinematic chain program demonstrated a significant improvement in throwing distance compared to the control group (p = 0.01). Additionally, the athletes in the experimental group reported higher levels of satisfaction with the exercise protocol (p = 0.005). These findings indicate that incorporating an 8-week Lower Body, Core and Upper Body kinematic chain exercise protocol into regular training sessions can lead to more pronounced improvements in sport-specific throwing performance among young shotput athletes.

Effects of Amantadine Sulfate on Motor Impairment and Execution of Motor Sequences in Patients With Parkinson Disease.

Intravenous application of amantadine sulfate induces a rapid improvement of motor behavior in patients with Parkinson disease. To determine efficacy of daily infusion of 200 mg amantadine sulfate on scored motor symptoms and performance of standardized movement sequences with components of low and high cognitive efforts. Thirty-one participants received infusions of amantadine sulfate in addition to their previous stable drug regimens on 3 consecutive days. Motor symptoms of upper limbs were determined with selected items of the part motor examination of the Unified Parkinson's Disease Rating Scale. Instrumental tasks were executed under cued conditions before and after the infusions. Scored motor symptoms and components with a need for a higher cognitive load became better. Performance of motion series, characterized by an automated set with low cognitive efforts, did not improve. Our trial outcomes suggest that the amantadine has a positive impact on cognitive abilities, drive, and vigilance, all of which are necessary for carrying out of higher brain functions.

Saturated nucleate flow boiling over a horizontal single and multi-cylinder under cross-flow condition

Present work focuses on simulating flow boiling of saturated liquid to reveal associated interfacial dynamics over horizontal heated circular cylinder under vertical flow condition. Varying liquid velocities and heat fluxes are considered for simulation to cover the study of the buoyancy and inertia-dominated mixed regimes. Cross-flow situation around isolated and vertically arranged in-line cylinders are modelled using Volume-of-Fluid (VOF) based interface capturing technique and Tanasawa’s phase change model. In case of a single cylinder, at lower cross-flow rate, individual bubble life cycle can be identified; whereas, at higher cross-flow rate, a continuous but wavy column is formed behind the cylinder due to formation of the stagnation zone and associated interfacial instability. The wall temperature and void fractions distributions from bottom to top of the cylinder differentiate the azimuthally varying heat transfer mechanisms. The inter-cylinder interfacial interaction in case of multi-cylinder is shown with sequential bubble movement illustrations. As moving from the bottom cylinder to top, the bubble size is observed to be increasing.

Cassette Loading Units for Hanging Devices of Breeding Seeders

The cassette loading device of breeding seeders is designed for sequential movement of cassettes with seed material to the sowing apparatus of the seeder. (Research purpose) The research purpose is reviewing of breeding seeders with a cassette loading unit for sowing machines and analyzing the technical solutions used in the devices. (Materials and methods) Conducted a review of cassette seeders for sowing various crops in breeding and primary seed production in order to find effective technical solutions. We compared different design solutions of cassette loading devices and chose the most rational design elements. (Results and discussion) The direction of improvement towards the robotization of a cassette device for loading breeding seeders was revealed, which increases the productivity of this auxiliary operation and reduces the use of manual labor. A classification of cassette loading devices used on breeding seeders was proposed. Formulated the technical task of developing a new device design. (Conclusions) We analyzed domestic and foreign technical solutions of cassette loading devices for sowing machines of breeding seeders, determined their technical and technological features. It was established that the improvement of the cassette device for loading breeding seeders, which increases the productivity of this auxiliary operation and reduces the use of manual labor, should be implemented in the direction of robotization of the process of operation of the cassette loading device. It was stated that in order to create a robotic cassette loading device, it is necessary to develop a manipulator controlled by a programmable electronic controller and a control system that ensures: moving cassettes along a guide (carrier) plate and feeding them to the holes for unloading seed material into the loading cylinders of sowing machines; sequential feeding of cassette blocks to a cassette table (in the working area); synchronization of the seeding machines with the operation of the loading device.

Online tangible robot programming: interactive automation method from teleoperation of manipulation task

Teleoperated robots are introduced to hospitals, restaurants and factories and are expected to learn the task from human-teleoperated data. The automation methods from teleoperation, as known as Programming by demonstration (PbD) or imitation learning, are recently proposed increasingly, but they are focused on simple task, and training phase and execution phase should be separated. In this paper, we propose online and interactive automation method from teleoperation, online tangible robot programming, for object manipulation with multiple procedures and physical contacts. We focus on target-objects' states, split the task into motion sequences based on the states' change, and program the order of motion sequences with tangible user interface. We also implement the user interruption for mode switching, and a user can switch between autonomous and teleoperation modes and correct the programmed motion orders while continuing the task execution. For the evaluation, we did the real-world experiments with dual-arm robot and demonstrate the usefulness and efficiency of online tangible robot programming by switching from autonomous mode to teleoperation mode and immediately programming and modifing the task procedures of the box rotating and piling tasks with two arms.

Electromyographic signals analysis to assess the response of a proprioceptive neuromuscular facilitation pattern execution.

Proprioceptive Neuromuscular Facilitation is a rehabilitation technique that consists of the stimulation of a healthy muscle in one extremity of the body to produce an activation effect of a damaged muscle in another extremity, laterally or contralaterally. The use of the analysis of the electromyographic response during the process allows us to describe and evaluate if the damaged muscle produces an activation. This paper presents the progress of the results of a clinical protocol where PNF is explored in healthy subjects, manipulating the upper limb, and recording the electromyographic response of the lower limbs in three different muscles in both inferior limbs. Four activation patterns (movement sequence) with three different stages with different intensities of resistance are considered. Lateral plane video recording analysis is performed to extract the trajectory of the manipulated limb and correlate this information with electromyography signals. The electromyography signals are analyzed in their temporal response by integrating the rectified and smoothed signal according to the beginning and end of the trajectory of the manipulated superior limb. Additionally, the spectrogram analysis of the signals is performed. The partial results of the protocol allow us to establish that the response in the most distal muscles to the manipulated upper limb has been non-significant for the subjects, compared to the response of the proximal muscles, where a relationship with activation and trajectory of the arm is observed. particularly in the process with greater resistance. The observed changes in signal frequency across the spectrogram correspond to low-frequency changes in the 15 to 20Hz band.Clinical Relevance- This work presents a strategy to assess the rehabilitation process based on proprioceptive neuromuscular facilitation by analyzing the time and time-frequency response of the electromyographic signal.

Mapping the neuroethological signatures of pain, analgesia, and recovery in mice

Ongoing pain is driven by the activation and modulation of pain-sensing neurons, affecting physiology, motor function, and motivation to engage in certain behaviors. The complexity of the pain state has evaded a comprehensive definition, especially in non-verbal animals. Here, in mice, we used site-specific electrophysiology to define key time points corresponding to peripheral sensitivity in acute paw inflammation and chronic knee pain models. Using supervised and unsupervised machine learning tools, we uncovered sensory-evoked coping postures unique to each model. Through 3D pose analytics, we identified movement sequences that robustly represent different pain states and found that commonly used analgesics do not return an animal's behavior to a pre-injury state. Instead, these analgesics induce a novel set of spontaneous behaviors that are maintained even after resolution of evoked pain behaviors. Together, these findings reveal previously unidentified neuroethological signatures of pain and analgesia at heightened pain states and during recovery.

Learning a covert sequence of effector movements: limits to its acquisition

Sequence learning in serial reaction time (SRT) tasks is an established, lab-based experimental paradigm to study acquisition and transfer of skills based on the detection of predictable regularities in stimulus and motor response sequences. Participants learn a sequence of targets and responses to these targets by associating the responses with subsequently presented targets. In the traditional paradigm, however, actions and response targets are directly related. In contrast, the present study asked whether participants would demonstrate acquisition of a sequence of effector movements of the left vs. right hand (e.g., hand sequence learning), whilst the actual targets and associated finger responses are unpredictable. Twenty-seven young adults performed a SRT task to visually presented characters with the index or middle fingers of both hands. While the specific fingers to respond with were randomly selected for each target presentation, both hands followed a covert sequence. We asked whether participants would learn the underlying hand sequence as demonstrated by shortened response latencies and increased accuracy compared to a fully randomized hand sequence. The results show sequence-specific learning effects. However, categorization of hand responses depending on the previous response suggested that learning occurred predominantly for subsequent finger responses of the same hand, which added to general hand-based priming. Nevertheless, a marginally significant effect was observed even for predictable shifts between hands when homologous fingers were involved. Our results thus suggest that humans are able to benefit from predictable within-hand finger shifts but less so for predicted shifts between hands.

Deep Learning-Assisted Gait Parameter Assessment for Neurodegenerative Diseases: Model Development and Validation.

Neurodegenerative diseases (NDDs) are prevalent among older adults worldwide. Early diagnosis of NDD is challenging yet crucial. Gait status has been identified as an indicator of early-stage NDD changes and can play a significant role in diagnosis, treatment, and rehabilitation. Historically, gait assessment has relied on intricate but imprecise scales by trained professionals or required patients to wear additional equipment, causing discomfort. Advancements in artificial intelligence may completely transform this and offer a novel approach to gait evaluation. This study aimed to use cutting-edge machine learning techniques to offer patients a noninvasive, entirely contactless gait assessment and provide health care professionals with precise gait assessment results covering all common gait-related parameters to assist in diagnosis and rehabilitation planning. Data collection involved motion data from 41 different participants aged 25 to 85 (mean 57.51, SD 12.93) years captured in motion sequences using the Azure Kinect (Microsoft Corp; a 3D camera with a 30-Hz sampling frequency). Support vector machine (SVM) and bidirectional long short-term memory (Bi-LSTM) classifiers trained using spatiotemporal features extracted from raw data were used to identify gait types in each walking frame. Gait semantics could then be obtained from the frame labels, and all the gait parameters could be calculated accordingly. For optimal generalization performance of the model, the classifiers were trained using a 10-fold cross-validation strategy. The proposed algorithm was also compared with the previous best heuristic method. Qualitative and quantitative feedback from medical staff and patients in actual medical scenarios was extensively collected for usability analysis. The evaluations comprised 3 aspects. Regarding the classification results from the 2 classifiers, Bi-LSTM achieved an average precision, recall, and F1-score of 90.54%, 90.41%, and 90.38%, respectively, whereas these metrics were 86.99%, 86.62%, and 86.67%, respectively, for SVM. Moreover, the Bi-LSTM-based method attained 93.2% accuracy in gait segmentation evaluation (tolerance set to 2), whereas that of the SVM-based method achieved only 77.5% accuracy. For the final gait parameter calculation result, the average error rate of the heuristic method, SVM, and Bi-LSTM was 20.91% (SD 24.69%), 5.85% (SD 5.45%), and 3.17% (SD 2.75%), respectively. This study demonstrated that the Bi-LSTM-based approach can effectively support accurate gait parameter assessment, assisting medical professionals in making early diagnoses and reasonable rehabilitation plans for patients with NDD.

Bond-slip at anchorage under unidirectional and bidirectional seismic excitation: Search for an efficient mathematical model

Reinforcement anchorage slip can significantly contribute to the seismic response of bridge columns, especially to residual deformation. For a reliable numerical model of slip action, the present study evaluates three advanced models, each founded on distinct principles: (a) a macroscopic model (M−I), (b) a microscopic model (M−II), and (c) a mixed model that employed micro-model material properties derived from a macro-model (M−III). The performance of each model under unidirectional shaking has been first validated. The M−I cannot account for bidirectional loading, and as such, the performance of the M−II and M−III has, for the first time, been rigorously examined against the two-component shake table test results. Beginning with the simplified pulses, the responses of representative bridge columns are computed to unidirectional and bidirectional shaking for several near-fault motions. The M−I, M−II, and M−III are fairly similar in mimicking the slip action for unidirectional loading. Also, the M−II and M−III can closely represent the responses to bidirectional shaking. A simple extension of the M−I is proposed to capture the effects of bidirectional loading. The performance of the M−I (extended), M−II, and M−III are shown similar from the viewpoint of representing (i) the seismic fragilities, (ii) the uncertainties of the influential parameters, and (iii) the consequences of a sequence of motions. The detailed scrutiny of a range of models helps select the most appropriate scheme in a specific design context. On the other hand, the less-rigourous model, viz., M−I with the proposed extension, can be used within the limitations of commercial software and can prove useful in routine design for bidirectional seismic action.