Human Motion Research Articles

Cellulose nanofibers/liquid metal hydrogels with high tensile strength, environmental adaptability and electromagnetic shielding for temperature monitoring and strain sensors

Hydrogel sensors are widely recognized in the fields of flexible electronics and human motion monitoring due to their multiple properties and potential applications. However, how to prepare hydrogels with multiple excellent properties simultaneously and how to improve the compatibility of conductive fillers with hydrogel matrices remain a major challenge. Therefore, in this work, liquid metal (LM) droplets stabilized by cellulose nanofibers (CNFs) were utilized to initiate the polymerization of acrylamide monomer (Am), which was used as a conductive filler. Meanwhile, reduced graphene oxide (rGO) was introduced to bridge the LM droplets. The hydrogels were then further crosslinked in glycerol. The constructed CNF@LM/polyacrylamide/rGO/gelatin/glycerol hydrogel possesses high tensile properties (>1317 %), high environmental adaptability (−80 to 80 °C), and adhesion properties for multifunctional sensing. What's more, it offers the high sensitivity of both a strain sensor and a temperature sensor for accurate monitoring of human movement at room temperature and even in extreme environments. In addition, this hydrogel has excellent electromagnetic shielding properties and antimicrobial properties. This research opens up a new direction for the preparation of multifunctional hydrogel sensors, expanding their applications in cutting-edge fields such as temperature monitoring, wearable smart devices, e-skin and intelligent robotics.

Skin-like dual-network gelatin/chitosan/emodin organohydrogel sensors mediated by Hofmeister effect and Schiff base reaction

Conductive gels have been extensively explored in the field of wearable electronics due to their excellent flexibility and deformability. Traditional gels constructed from synthetic networks pose risks to biosecurity due to residual monomers like acrylamide, while pure biological hydrogels are plagued by inadequate mechanical performance. This study explores an innovative strategy, employing a dual-network (DN) system with purely biological components, as a superior alternative to conventional synthetic networks. By integrating gelatin and chitosan, two natural polymers with inherent biocompatibility and advantageous biomedical properties, this approach successfully avoids the toxic risk of synthetic polymers. By utilizing emodin, a natural extract from Rheum officinale, as a cross-linking agent for chitosan by Schiff base reactions, and Hofmeister effect of gelatin induced by sodium carbonate, the DN gelatin/chitosan/emodin organohydrogels achieve ultrahigh tensile strength (up to 9.45 MPa), tunable moduli (ranging from 0.07 to 3.42 MPa), excellent toughness (∼9.64 MJ/m3), and high ionic conductivity (7.63 mS/cm). Remarkably, these conductive organohydrogels also exhibit high sensitivity (gauge factor up to 1.5) and ultrahigh linearity (R2 up to 0.9995), making them promising candidates for soft human-motion sensors capable of accurately detecting and monitoring human movements in real time with high sensitivity and durability.

Starch/polyacrylamide hydrogels with flexibility, conductivity and sensitivity enhanced by two imidazolium-based ionic liquids for wearable electronics: Effect of anion structure

To meet the growing demands for sustainable and eco-friendly wearable electronics, biopolymer-based hydrogels have attracted much attention. As one of the most abundant sources of biopolymers, starch has the advantages of low-cost, renewability, biocompatibility and biodegradability. However, mechanical fragility, low conductivity and low sensitivity limited the application of starch-based hydrogels. Herein, two imidazolium-based ionic liquids with different anions (chloridion and acetate) were introduced into corn starch/polyacrylamide hydrogels. The mechanical properties (the maximum elongation: 515.4 %), conductivity (the maximum value: 3.1 S·m−1) and sensitivity (the maximum gauge factor value: 9.3) of the hydrogel were enhanced by the two ionic liquids and proved by the microcosmic characterizations and theoretical simulations (DFT). The two ionic liquids varied in their impacts on the above properties of the hydrogels due to the different anion structure. In mechanical properties, acetate was dominant over chloridion, while the opposite was true for conductivity. Based on the above properties of the starch-based hydrogels, wearable electronics were constructed for detecting human joint motions, subtle expressions, temperature and touch screen operations. This work not only provides novel starch-based hydrogels as candidates for the wearable electronics, but also lays a theoretical foundation for the application of ionic liquids in biopolymer-based materials.

Multifunctional double network hydrogel with multi-directional actuation and high-precision sensing

Conductive hydrogels have gained great attention for applications in flexible electronics, electronic skins, and as human-machine interfaces. However, poor environmental tolerance of traditional hydrogels and defects in hydrogel actuator make them unfit for use in certain environments. Herein, a small natural molecule, proline (ZP), was introduced into the PAA/CS dual network system to prepare multifunctional hydrogel materials with high concentration of physically crosslinked metal coordination points. The elongation at break and mechanical strength of the hydrogel with optimized structure were 1277 % and 202 kPa, respectively. The effect of ZP on the destruction of hydrogen bonds of free water in the hydrogel provided the hydrogel with excellent temperature resistance. Multiple reversible interactions endowed the PAA/CS/Al0.5/ZP8 hydrogel with excellent self-healing properties at room temperature and even at −40 ℃. The PAA/CS/Al0.5/ZP8 hydrogel sensor with excellent comprehensive performance could be applied for the monitoring of a variety of human motions, small shape changes of flexible objects, and detection of vibrations in rigid pipes. The interactions between Fe3+, ascorbic acid, and -COOH groups on the hydrogel surface were used to record information and erase it repeatedly at low temperature and showed excellent shape memory. Based on the self-healing property, the hydrogel with gradients of swelling rates and water loss rates was assembled into a double-layer actuator, which could complete the actuation process in opposite directions in air and also underwater. This work provides a new idea for the design of soft robots and intelligent switches.

Personalized assist-as-needed dressing assistance robot without human modeling using Rowat-Selverston CPG controller

Dressing is a critical component of Activity of Daily Living (ADLs). The development of assistive technology for dressing tasks is urgently needed from the viewpoint of privacy, for example, to prevent the wearer from being seen in the nude. Despite their importance, these technologies are underutilized in garment donning compared to other ADLs. The reasons for the lack of utilization of assistive technology are that the task is complex, as it involves handling individuals with large individual differences in height and symptoms and requires manipulation of flexible clothing. Addressing these variances presents a formidable academic challenge. Our previous study identified periodic patterns in human movements during robot-assisted dressing and noted individual variability in these patterns. Capitalizing on this finding, we propose a novel control strategy for a robot that adapts to individual needs during dressing, employing the ‘Assist-As-Needed’ (AAN) principle from physical therapy. The proposed control method uses Central Pattern Generators (CPG) that can be synchronously controlled in response to external forces, enabling model-free control without human modeling. We utilize the Rowat-Selverston CPG model, which is recognized for its adaptive response to human motions in human-robot interactions such as with handshaking robots. A simulator of the Rowat-Selverston CPG was created. The output of the CPG was confirmed by inputting the data set obtained in previous studies, and the parameters were adjusted. The CPG output was then applied to the recorded target joint trajectory for the dressing assistance. We prepared a replay of the default trajectory, a trajectory with simple harmonic motion applied, and a trajectory with CPG output applied, and confirmed whether individual adaptation according to the AAN was possible through subject experiments. The experimental results showed that the Rowat-Selverston CPG control method enables individual adaptive dressing assistance according to the AAN principle. This study summarizes these methods and results and contributes to the realization of an individually adaptive system that follows the AAN principle, especially in developing assistive technology.

No effects of abiotic and anthropogenic factors on reef-associated neonate shark abundance within a shark nursery-area system

Context Coastal habitats function as shark nursery areas; however, coastal habitats can experience extreme variation in abiotic conditions and are susceptible to human disturbances. Aims Drivers of abundance were tested within a shark nursery-area system in two populations of reef-associated neonate sharks, namely, blacktip reef sharks (Carcharhinus melanopterus) and sicklefin lemon sharks (Negaprion acutidens). Methods Catch data from a fisheries-independent gill-net survey (n = 90 sets from October 2018 to March 2019) at 10 sites around Moorea, French Polynesia, were used to test for associations between shark abundance and abiotic conditions (temperature, oxygen, pH, salinity, lunar phase and depth). Historical levels of fin-fish fishing effort, trampling (i.e. human movement through habitat), and coastal artificialisation (i.e. walls and embankments) estimated for each site were used to test for anthropogenic effects on shark abundance. Key results There were no effects of any abiotic or anthropogenic factor on abundance of either species. Conclusions Previous work corroborates our findings by demonstrating neonate sharks’ physiological tolerance to extreme abiotic conditions and high survival in response to anthropogenic stressors. Alternatively, populations are already degraded from decades of coastal development. Implications These data can aid in predicting the use of coastal habitats as shark nursery areas.

MXene-Fiber Composite Membranes for Permeable and Biocompatible Skin-Interfaced Iontronic Mechanosensing.

Artificial ionic sensory systems, bridging the divide between biological systems and electronics, mimic human skin functions but face critical challenges with biocompatibility, comfort, signal stability, and simplifying packaging. Here, we present a simple and permeable skin-interfaced iontronic mechanosensing (SIIM) architecture that integrates human skin as natural ionic material and hierarchically porous MXene-fiber composite membranes as sensing electrodes. The SIIM system eliminates complex ionic material design and multilayer matrix, exhibiting ultrahigh pressure sensitivities (5.4 kPa-1, <75 Pa), a low detection limit (6 Pa), excellent output stability along with high permeability to minimize the impact of sweating on sensing. The noncytotoxic nature of SIIM electrodes ensures excellent biocompatibility (>97% cell coincubational viability), facilitating long-term wearability and high biosafety. Furthermore, the scalable SIIM configuration integrated with matrix smart gloves, effectively monitors human physical movements. This SIIM-based sensor with marked sensing capabilities, structural simplicity, and scalability, holds promising potential in diverse wearable applications.

Multifunctional Nano‐Conductive Hydrogels With High Mechanical Strength, Toughness and Fatigue Resistance as Self‐Powered Wearable Sensors and Deep Learning‐Assisted Recognition System

AbstractHigh mechanical strength, toughness, and fatigue resistance are essential to improve the reliability of conductive hydrogels for self‐powered sensing. However, achieving mutually exclusive properties simultaneously remains challenging. Hence, a novel directed interlocking strategy based on topological network structure and mechanical training is proposed to construct tough hydrogels by optimizing the network structure and modulating the orientation of molecular chains. Combining Zn2+ crosslinked cellulose nanofibers (CNFs) and a polyacrylamide‐poly(vinyl alcohol) double‐network, the unique interlocked‐network structure exhibits an enhanced toughening effect due to hydrogen bonding and metal‐ligand interactions. The aligned nanocrystalline domains achieved by training further contribute to an increase in the toughness and fatigue thresholds. This innovative approach synergistically enhances the mechanical properties of the nano‐conductive hydrogel, achieving a maximum tensile strength of 4.98 MPa and a toughness of 48 MJ m−3. Notably, the CNFs template with anchored polyaniline, when oriented through mechanical training, forms a unique directional conductive pathway, which significantly enhances the power output performance. Besides, a motion recognition system based on a self‐powered sensing device is designed with the assistance of deep learning techniques to accurately identify human motion behaviors. This work showcases a potentially transformative flexible electronic material for self‐powered sensing systems and intelligent recognition systems.

A Highly Robust, Multifunctional, and Breathable Bicomponent Fibers Thermoelectric Fabric for Dual-Mode Sensing.

Wearable thermoelectric (TE) materials are seen as excellent candidates for flexible electronics because of their unique self-powered properties, multistimulus sensing and human waste heat conversion. However, currently reported flexible TE materials still face challenges such as poor durability, uncomfortable wearing and sensing signals crosstalking each other. Herein, this study describes a hot-air cross-linking method for the preparation of multifunctional TE fabrics with enhanced durability. Poly(ethylene terephthalate) (PET) fibers with core and sheath structures having different melting points were selected as flexible substrates. Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) and single-walled carbon nanotubes (SWCNTs) were embedded stably on the surface of the sheath layer in the presence of heat treatment. The fiber-welded structure created by thermal cross-linking improves the durability of TE fabrics, including consistent mechanical and electrical properties after a 6 h wash test and 6000 compression cycles. The unique fiber structure of TE fabrics ensures excellent breathability (313.7 mm s-1 at 200 Pa), which meets the breathability requirements for human wear. In addition, the fiber-prepared sensors have excellent compressive strain response (20 ms response time and 30 ms recovery time) and precise temperature discrimination (0.17 K minimum discrimination temperature) for accurate real-time monitoring of the sensed signals. Thus, the TE fabrics can be used for human motion recognition, including pulse monitoring, sign language expression, and motions in joint areas. Moreover, the fabricated wearable TE device is connected to a Bluetooth module for wireless transmission, which can be used for mechanical and temperature sensing of the robot arm without signals crosstalking. This new durable TE fabric paves the way for the next generation of smart wearable technology.

Robust, transparent, self-healable, recyclable all-starch-based gel with thermoelectric capability for wearable sensor

Conventional all-starch-based (ASB) gels are weak and lack ductility. The preparation of a robust ASB gel with multi-functionalities e.g., self-healing, anti-freezing, conductivity, and so forth, is highly desirable but challenging. Herein, a new kind of ASB gel was prepared by gelatinizing starch in urea and choline chloride solution (UC) with the aid of water. Its tensile strength was up to 1.08 MPa with a tensile strain of 313 %, and this value hardly changed after 10 days ageing. A high healing efficiency of 98 % can be achieved after 1 h of healing at room temperature, and the healed tensile strength reaches up to ca. 1.06 MPa, which is almost the highest value for ASB gel. The resultant ASB gel can surfer from bending and twisting at −80 °C. Moreover, ASB gel also exhibits excellent biocompatibility and biodegradability. In addition, UC endowed the ASB gel with ion conductivity, allowing it to be used as a flexible strain sensor to monitor human movement. The ion-conductive ASB gel also exhibited thermoelectric ability with a Seebeck coefficient of 2.5 mV K−1, which can be further improved to 5 mV K−1 with a maximum output voltage of 252 mV by introducing a gradient of ionic concentration.

Immigrants, asylum-seekers, and refugees: Navigating conceptual challenges through multidimensional migration space-time

Migration is a pervasive social, political, and economic experience. Despite its ubiquity, the complexities of migration present challenges for scholars. Ongoing debates revolve around defining central categories that describe and understand human movement. Concepts such as migrant, asylum-seeker, and refugee are fraught with tensions and overlapping characteristics. Disaggregating these categories is complex due to the messy and varied nature of individuals&amp;rsquo; lived experiences. This complexity prompts scholars to critically examine how these categories are constructed and applied. Considering this, I argue that reconceptualizing migration categories as interconnected positions within a temporally and geographically contingent multidimensional migration space-time allows scholars to better examine the complexities of movement. Moreover, the intricate nature of migration necessitates the construction and reconstruction of adaptable concepts to better understand highly contextual experiences. Therefore, researchers must design research projects that can capture the nuances, tensions, and contradictions inherent in migration experiences, even when these nuances do not align with narrow, instrumentalized definitions used in legal regimes and public policy. While more flexible concepts offer valuable opportunities for deeper insights, there are also significant risks to consider.

Isotropically Polarized Bipiezoelectric Polyacrylonitrile-Poly(vinylidene Fluoride) Advanced Triboelectric Nanogenerator for Operation in High-Humidity Environments.

Conventional hybrid piezo-triboelectric nanogenerators (PTNGs) have potential applications as energy supply devices for microelectronic devices, but their low power density and unstable performance under high-humidity conditions are challenges that need to be solved. Here, we report a novel flexible hybrid bifiezo-triboelectric nanogenerator (Bi-PTNG) based on isotropic polarization design of piezoelectric PVDF and PAN nanofiber membranes, which greatly improves power density of devices and performances in high-humidity conditions. The performance enhancement mechanism of the Bi-PTNG was investigated by model analysis, experimental measurements, and simulations. The results showed that the power density output of Bi-PTNG increased by approximately 88% compared to that of a depolarized PAN/PVDF-based triboelectric nanogenerator (TENG). The application studies demonstrated that a 2 × 2 cm2 Bi-PTNG can be directly used to run more than 120 commercial LEDs, and this highly flexible and breathable all-fiber device can be integrated into clothing or insoles to monitor human movement in high-humidity conditions. This work not only provides an effective strategy for enhancing the power output of TENGs and advancing their practical applications but also offers robust guidance for the material selection and optimization of the polarization direction in such nanogenerators.

An Editorial for the Special Issue of “Recent Advances in Biomechanics of Human Movement and Its Clinical Applications”

An Editorial for the Special Issue of “Recent Advances in Biomechanics of Human Movement and Its Clinical Applications”

Wild Boar Proves High Tolerance to Human-Caused Disruptions: Management Implications in African Swine Fever Outbreaks.

Currently, African swine fever (ASF), a highly fatal disease has become pervasive, with outbreaks recorded across European countries, leading to preventative measures to restrict wild boar (Sus scrofa L.) movement, and, therefore, keep ASF from spreading. This study aims to detail how specific human activities-defined as "car", "dog", "chainsaw", and "tourism"-affect wild boar behavior, considering the disturbance proximity, and evaluate possible implications for wild boar management in ASF-affected areas. Wild boar behavior was studied using advanced biologging technology. This study tracks and analyzes wild boar movements and behavioral responses to human disturbances. This study utilizes the dead reckoning method to precisely reconstruct the animal movements and evaluate behavioral changes based on proximity to disturbances. The sound of specific human activities was reproduced for telemetered animals from forest roads from different distances. Statistical analyses show that wild boars exhibit increased vigilance and altered movement patterns in response to closer human activity, but only in a small number of cases and with no significantly longer time scale. The relative representation of behaviors after disruption confirmed a high instance of resting behavior (83%). Running was the least observed reaction in only 0.9% of all cases. The remaining reactions were identified as foraging (5.1%), walking (5.0%), standing (2.2%), and other (3.8%). The findings suggest that while human presence and activities do influence wild boar behavior, adherence to movement restrictions and careful management of human activity in ASF-infected areas is not a necessary measure if human movement is limited to forest roads.

Diplomatic webs: the influential figures shaping U.S. policy in Israel, Qatar, and Iraq

Abstract Purpose This study examines the Twitter/X networks of U.S. ambassadors in Israel, Iraq, and Qatar from 2017 to 2024, aiming to assess how digital diplomacy is conducted through these platforms. Utilizing Sprinklr, we gathered 586,736 mentions involving the ambassadors’ Twitter handles, with a focus on evaluating the influence and communication strategies within these networks. Design/methodology/approach We analyzed a random sample of 30,000 tweets from the collected data using network analysis techniques. This approach enabled the examination of centrality metrics within the ambassadors’ digital networks, providing insights into the influence patterns and communicative interactions via Gephi. Findings The analysis revealed a significant influence of state actors and established political elites who predominantly engage in unidirectional communication, despite the platforms’ capabilities for interactive and reciprocal dialogues. We identified elected officials and specific non-governmental organizations as key actors shaping the diplomatic narratives, highlighting the diverse yet controlled actor interplay in digital diplomacy. Practical implications This research underscores the need for strategic adjustments in digital diplomacy practices to enhance interaction and inclusivity. Our work provides policymakers with insights into leveraging digital platforms for more effective and dynamic diplomatic exchanges. Social implications The study illuminates the role of digital platforms as critical venues for shaping diplomatic narratives by both state and non-state actors. Notably, our findings highlight the use of hashtags in advancing human rights movements and in discussions surrounding the Israel–Palestine conflict, demonstrating hashtags’ impact on global and regional advocacy efforts. Originality/value This research offers a unique perspective on the integration of traditional diplomatic roles with contemporary digital strategies, particularly highlighting the constraints and potentials within Middle Eastern contexts. We suggest ways to enhance the inclusivity and effectiveness of diplomatic engagements through improved social media utilization, thereby contributing to the evolving field of international relations and public diplomacy.

Evaluating the impact of human movement-induced airflow on particle dispersion: A novel real-time validation using IoT technology

Human movement significantly influences local airflow and particle dispersion in indoor environments. Therefore, this study integrates the dynamics of human walking into the analysis of airborne infection risks in a positive pressure isolation ward designed for the protection of immunocompromised patients. Real-time data collection was performed using an anemometer and IoT-based PM sensors to measure airflow velocities and particulate matter (PM) concentrations within a full-scale experimental chamber. Computational Fluid Dynamics (CFD) was used for the numerical simulations, and a User-Defined Function (UDF) code simulated the translational movement of a manikin. The results demonstrated strong agreement with the experimental data, thereby validating the airflow turbulence and Lagrangian-based Discrete Phase Model (DPM) in predicting the airflow velocities and particle transport. The study revealed that human walking substantially enhanced particle dispersion distance by 10-fold compared to static conditions, primarily attributed to intensified air mixing induced by the body movement. Furthermore, the CFD analysis underscored that the direction of walking plays a crucial role in airborne transmission. Specifically, walking away from a patient did not elevate infection risk, whereas approaching a patient significantly increased particle deposition in the patient-occupied region. This study highlights the critical need to consider both movement patterns and directional flow in managing airborne infection risks, contributing to the development of more effective infection control strategies in healthcare settings.

Machine Learning-Assisted Gesture Sensor Made with Graphene/Carbon Nanotubes for Sign Language Recognition.

Gesture sensors are essential to collect human movements for human-computer interfaces, but their application is normally hampered by the difficulties in achieving high sensitivity and an ultrawide response range simultaneously. In this article, inspired by the spider silk structure in nature, a novel gesture sensor with a core-shell structure is proposed. The sensor offers a high gauge factor of up to 340 and a wide response range of 60%. Moreover, the sensor combining with a deep learning technique creates a system for precise gesture recognition. The system demonstrated an impressive 99% accuracy in single gesture recognition tests. Meanwhile, by using the sliding window technology and large language model, a high performance of 97% accuracy is achieved in continuous sentence recognition. In summary, the proposed high-performance sensor significantly improves the sensitivity and response range of the gesture recognition sensor. Meanwhile, the neural network technology is combined to further improve the way of daily communication by sign language users.

A robust myoelectric pattern recognition framework based on individual motor unit activities against electrode array shifts

Background and objectiveElectrode shift is always one of the critical factors to compromise the performance of myoelectric pattern recognition (MPR) based on surface electromyogram (SEMG). However, current studies focused on the global features of SEMG signals to mitigate this issue but it is just an oversimplified description of the human movements without incorporating microscopic neural drive information. The objective of this work is to develop a novel method for calibrating the electrode array shifts toward achieving robust MPR, leveraging individual motor unit (MU) activities obtained through advanced SEMG decomposition. MethodsAll of the MUs from decomposition of SEMG data recorded at the original electrode array position were first initialized to train a neural network for pattern recognition. A part of decomposed MUs could be tracked and paired with MUs obtained at the original position based on spatial distribution of their MUAP waveforms, so as to determine the shift vector (describing both the orientation and distance of the shift) implicated consistently by these multiple MU pairs. Given the known shift vector, the features of the after-shift decomposed MUs were corrected accordingly and then fed into the network to finalize the MPR task. The performance of the proposed method was evaluated with data recorded by a 16 × 8 electrode array placed over the finger extensor muscles of 8 subjects performing 10 finger movement patterns. ResultsThe proposed method achieved a shift detection accuracy of 100 % and a pattern recognition accuracy approximating to 100 %, significantly outperforming the conventional methods with lower shift detection accuracies and lower pattern recognition accuracies (p < 0.05). ConclusionsOur method demonstrated the feasibility of using decomposed MUAP waveforms’ spatial distributions to calibrate electrode shift. This study provides a new tool to enhance the robustness of myoelectric control systems via microscopic neural drive information at an individual MU level.

Flexible and robust polypyrrole/cross-linked collagen sponge with collagen aggregates as building blocks for piezoresistive sensing

The polypyrrole/cross-linked collagen sponge (PPy/CCS) was synthesized through in-situ polymerization of PPy nanoparticles onto the collagen skeleton composed of mesoscopic collagen aggregates (CAs) as building blocks, which was employed as a sensing material. The structure and morphology of PPy/CCS were characterized using scanning electron microscopy (SEM), Fourier infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and thermal analysis (TA). The conductivity and mechanical properties of PPy/CCS can be regulated by adjusting the polymerization conditions. The PPy/CCS treated with glycerin aqueous solution exhibits exceptional recovery performance under 10,000 compression stress cycles, with a short response time of 420 ms and recovery time of 400 ms, along with high sensitivity of 1.13 kPa−1 within a control pressure range of 0–43 kPa. The assembled PPy/CCS-based sensors enable monitoring of human movement behavior and achieving multi-touch control, thereby demonstrating significant potential in flexible wearable devices and human–machine interaction.

Enhancing Digital Twins with Human Movement Data: A Comparative Study of Lidar-Based Tracking Methods

Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as “flickering” in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy.