Finger Movements Research Articles

Wearable Fabric System for Sarcopenia Detection

Sarcopenia has been a serious concern in the context of an increasingly aging global population. Existing detection methods for sarcopenia are severely constrained by cumbersome devices, the necessity for specialized personnel, and controlled experimental environments. In this study, we developed an innovative wearable fabric system based on conductive fabric and flexible sensor array. This fabric system demonstrates remarkable pressure-sensing capabilities, with a high sensitivity of 18.8 kPa−1 and extraordinary stability. It also exhibits excellent flexibility for wearable applications. By interacting with different parts of the human body, it facilitates the monitoring of various physiological activities, such as pulse dynamics, finger movements, speaking, and ambulation. Moreover, this fabric system can be seamlessly integrated into sole to track critical indicators of sarcopenia patients, such as walking speed and gait. Clinical evaluations have shown that this fabric system can effectively detect variations in indicators relevant to sarcopenia patients, proving that it offers a straightforward and promising approach for the diagnosis and assessment of sarcopenia.

Finger contact keyboard for typing with tiny movement recognition

Hemiplegic patients often struggle with typing rapidly and accurately using a standard keyboard. This study developed a keyboard in continuous contact with the fingers, allowing for easier and more effective typing. With the proposed keyboard, the user types with their healthy hand and paralyzed hand. The hardware and software of the finger contact keyboard were investigated. In deciding the hardware, we measured the hand shape, finger speed, and muscle fatigue using electromyography, magnetic positioning sensors, and force sensors for eight participants. Using the Pareto solution, we optimized the keyboard’s structure to maximize finger movement speed and minimize muscle fatigue. As the software of the proposed keyboard, we developed an algorithm implementing a neural network to identify intentional typing and tested the algorithm on five participants. The highest average discrimination accuracy was 99.3% when the force threshold was approximately 1.32 N. The mean accuracy achieved using the neural network was 90.8%, which is higher than that achieved using the threshold algorithm (80.4%). In 40 trials, the proposed keyboard achieved the same accuracy and speed as the standard keyboard, and the input time for a patient with hemiplegia was reduced by 16.2%.

MOGrip: Gripper for multiobject grasping in pick-and-place tasks using translational movements of fingers.

Humans use their dexterous fingers and adaptable palm in various multiobject grasping strategies to efficiently move multiple objects together in various situations. Advanced manipulation skills, such as finger-to-palm translation and palm-to-finger translation, enhance the dexterity in multiobject grasping. These translational movements allow the fingers to transfer the grasped objects to the palm for storage, enabling the fingers to freely perform various pick-and-place tasks while the palm stores multiple objects. However, conventional grippers, although able to handle multiple objects simultaneously, lack this integrated functionality, which combines the palm's storage with the fingers' precise placement. Here, we introduce a gripper for multiobject grasping that applies translational movements of fingertips to leverage the synergistic use of fingers and the palm for enhanced pick-and-place functionality. The proposed gripper consists of four fingers and an adaptive conveyor palm. The fingers sequentially grasp and transfer objects to the palm, where the objects are stored simultaneously, allowing the gripper to move multiple objects at once. Furthermore, by reversing this process, the fingers retrieve the stored objects and place them one by one in the desired position and orientation. A finger design for simple object translating and a palm design for simultaneous object storing were proposed and validated. In addition, the time efficiency and pick-and-place capabilities of the developed gripper were demonstrated. Our work shows the potential of finger translation to enhance functionality and broaden the applicability of multiobject grasping.

An Underactuated Dexterous Hand with Novel Bidirectional Self-Locking Joints for Multiple Fingertip Active Motion Trajectories

This paper proposes an underactuated dexterous hand with novel bidirectional self-locking joints (BSJs) that enable multiple fingertip motion trajectories. The BSJ design integrates a locking wheel, rack, finger side walls, and a self-holding electromagnetic actuator, combining rack-and-pinion transmission with friction self-locking principles. Building on the BSJ concept, an underactuated dexterous hand is developed. The study begins with an analysis of BSJ’s deviation angle, establishing the minimum deviation angle critical to its operation. A detailed mechanical model of a BSJ is formulated, and its parameters are quantitatively analyzed to determine a safety static friction coefficient (0.177). Five distinct finger motion modes are designed and kinematic analysis focuses on the index finger and the generation of 57 unique fingertip active motion trajectories. Experimental validation included single finger performance tests that confirmed the diversity of fingertip trajectories and the hand’s ability to withstand loading in both forward and reverse directions. Through envelope and precision grasping experiments, the dexterous hand demonstrated its adaptability and ability to grasp objects of various sizes and shapes, such as strawberries, apples, student ID cards, and water bottles. This capability underscores its potential for a wide range of applications, from prosthetic hands for rehabilitation, where precision and adaptability are key, to robotic hands in industrial automation, offering flexibility in diverse tasks.

Effects of physical practice on the duration of motor imagery

Motor Simulation Theory proposes that imagined actions are produced using the brain’s motor system, and should therefore always be temporally equivalent to physical movements. However, empirical results are not always consistent with this prediction. Studies indicate that the durations of unfamiliar imagined actions are over-estimated, whereas the durations of more familiar actions may be closer to (or even faster than) actual movement execution. We therefore examined the effects of different levels of practice on the durations of both physically performed and imagined actions. Participants (N=31) completed an initial assessment in which the durations of physically performed and imagined finger movement sequences were measured. Participants then completed three days of physical training in which different sequences received either extensive training (150 repetitions/session), minimal training (10 repetitions/session), or no training. In a subsequent assessment session, we found that the time taken to both physically execute and imagine performing sequences decreased with training. However, contrary to the predictions of Motor Simulation theory, imagined movement durations consistently over-estimated those of physically performed movements. While the difference in the timing of imagined and physically executed movements decreased between the initial and final assessment, this effect was not modulated by training. These results extend our understanding of the relationship between motor imagery and physical practice, and highlight a key limitation in the predictions of Motor Simulation Theory.

Revision Surgery for Scar Contracture and Web Creep after Syndactyly Release.

Background: Revision surgery after syndactyly separation is challenging. Web creep and scarring have a great impact on function and appearance of the hand. There is a paucity of literature on revision surgery for syndactyly. The aim of this study is to present the outcomes of revision surgery for syndactyly. Methods: This retrospective study included patients who required revision surgery after syndactyly release for web creep, scar contracture affecting motion and/or bony deformities. Web creep was graded using the Withey classification and scars were graded as minimal, mild, moderate and severe based on the impact on range of motion (ROM). All patients underwent dorsal and volar triangular flaps for correction for web creep, multiple Z-plasty and/or proximal interphalangeal joint (PIPJ) release for correction of scar contractures and osteotomy for correction of angular/rotational bony deformities. Improvement in web creep grading, scar contracture and change in angular and/or rotational deformities were recorded. Results: The study included 9 hands in 7 patients who required surgery for 13 web creeps (four grade 2, four grade 3 and five grade 4), 20 scar contractures (five mild, seven moderate and eight severe), 6 angular (five ≤15° and one = 40°) and 1 rotational deformity. All web creeps improved to grade 1. Out of the 20 scar contractures, 14 improved to mild and 6 to moderate. Two patients underwent corrective osteotomy, one for rotational deformity and one for a 40° angular deformity which improved to 5°. Conclusions: Double opposing triangular flap presents a good option for revision of the web with no recurrence of web creep after a 1-year follow-up period. We suggest a grading system which assesses the effect of scarring on finger motion. Finally, avoiding straight volar suture lines decreases the incidence of recurrence. Level of Evidence: Level IV (Therapeutic).

Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG

Subjectivity has been an inherent issue in the conventional Fugl-Meyer assessment, which has been the focus of impairment-level recognition in several studies. This study continues our previous work on the use of EMG to recognize finger movement impairment levels. In contrast to our previous work, this study provided a better and more reliable recognition result with improved experimental settings, such as an increased sampling frequency, EMG channels, and extensive patient data. This study employed two data processing mechanisms, inter-subject cross-validation (ISCV) and data-scaled inter-subject cross-validation (DS-ISCV), resulting in two evaluation methods. The machine learning algorithms employed in this study were SVM, random forest (RF), and multi-layer perceptron (MLP). MLP_ISCV achieved the highest average recall score of 0.73 across impairment levels in the spherical grasp task. Subsequently, the highest average recall score of 0.72 among non-majority classes was achieved by SVM_DS-ISCV in the mass extension task. The cross-validation result shows that the proposed method effectively handled the imbalanced dataset without being biased toward the majority class. The proposed method demonstrated the potential to assist doctors in clarifying the subjective assessment of finger movement impairment levels.

Triboelectric-Inertial Sensing Glove Enhanced by Charge-Retained Strategy for Human-Machine Interaction.

As technology advances, human-machine interaction (HMI) demands more intuitive and natural methods. To meet this demand, smart gloves, capable of capturing intricate hand movements, are emerging as vital HMI tools. Moreover, triboelectric-based sensors, with their self-powered, cost-effective, and material various characteristics, can offer promising solutions for enhancing existing glove systems. However, a key limitation of these sensors is that charge leakage in the measurement circuit results in capturing only transient signals, rather than continuous changes. To address this issue, a charge-retained circuit that effectively prevents triboelectric signal attenuation is developed, enabling accurate measurement of continuous finger movements. This innovation forms the foundation of a highly integrated smart glove system, enhancing HMI functionality by combining continuous triboelectric signals with inertial sensor data. The system showcases a diverse range of applications, including complex robotic control, virtual reality interaction, smart home lighting adjustments, and intuitive interface operations. Furthermore, by leveraging artificial intelligence (AI) techniques, the system achieves accurate recognition of complex sign language with an impressive 99.38% accuracy. This work presents a charge-retained approach for continuous sensing with triboelectric-based sensors, offering valuable insights for developing future multifunctional HMI and sign language recognition systems. The proposed smart glove system, with its dual-mode sensing and AI integration, holds great potential for revolutionizing various domains and enhancing user experiences.

Effectiveness of Comprehensive Video Datasets: Toward the Development of an Artificial Intelligence Model for Ultrasonography-Based Severity Diagnosis of Carpal Tunnel Syndrome.

Advances in diagnosing carpal tunnel syndrome (CTS) using ultrasonography (US) and artificial intelligence (AI) aim to replace nerve conduction studies. However, a method for accurate severity diagnosis remains unachieved. We explored the potential of comprehensive video data formats for constructing an effective model for diagnosing CTS severity. We studied 75 individuals (52 with CTS) from 2019 to 2022, categorizing them into 3 groups based on disease severity. We recorded 132 US videos of carpal tunnel during finger movement. Features of the median nerve (MN) were extracted from automatically segmented US video frames, from which 3 datasets were created: a comprehensive video dataset with full information, a key metrics dataset, and an initial frame dataset with the least information. We compared the accuracy of machine learning algorithms for classifying CTS severity into 3 groups across these datasets using 63-fold cross-validation. The cross-sectional area of the MN correlated with severity (P < .05) but MN displacement did not. The algorithm using the comprehensive video dataset exhibited the highest sensitivity (1.00) and accuracy (0.75). Our study demonstrated that utilizing comprehensive video data enables a more accurate US-based diagnosis of CTS severity. This underscores the value of capturing the patterns of MN deformation and movement, which cannot be captured by representative metrics such as medians or maximums. By further developing an AI model based on our findings, a simpler and painless method for assessing CTS severity can be achieved.

Multimodal hand/finger movement sensing and fuzzy encoding for data‐efficient universal sign language recognition

AbstractWearable sign language recognition helps hearing/speech impaired people communicate with non‐signers. However current technologies still unsatisfy practical uses due to the limitations of sensing and decoding capabilities. Here, A continuous sign language recognition system is proposed with multimodal hand/finger movement sensing and fuzzy encoding, trained with small word‐level samples from one user, but applicable to sentence‐level language recognition for new untrained users, achieving data‐efficient universal recognition. A stretchable fabric strain sensor is developed by printing conductive poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) ink on a pre‐stretched fabric wrapping rubber band, allowing the strain sensor with superior performances of wide sensing range, high sensitivity, good linearity, fast dynamic response, low hysteresis, and good long‐term reliability. A flexible e‐skin with a homemade micro‐flow sensor array is further developed to accurately capture three‐dimensional hand movements. Benefitting from fabric strain sensors for finger movement sensing, micro‐flow sensor array for 3D hand movement sensing, and human‐inspired fuzzy encoding for semantic comprehension, sign language is captured accurately without the interferences from individual action differences. Experiment results show that the semantic comprehension accuracy reaches 99.7% and 95%, respectively, in recognizing 100 isolated words and 50 sentences for a trained user, and achieves 80% in recognizing 50 sentences for new untrained users.image

A novel approach to enhancing smart stiffness of soft robotic gripper fingers for wider grasping capability

AbstractThis paper presents a proposed design of soft gripper fingers with adjustable stiffness that could be employed in the applications requiring adaptable and stable grasping. The main idea is to combine the under-actuated cable driven mechanism of a soft gripper finger with particle and layer jamming mechanisms to create a new grasping function with variable stiffness for different manipulation requirements. The movement of the soft gripper finger is produced by a cable-driven mechanism. However, particle and layer jamming chambers were embodied as a variable stiffness mechanism for the variable stiffness function. A single soft gripper finger module was developed and tested with particle and layer jamming chamber attached to it. The stiffness and response time of the soft gripper finger were measured in three distinct configurations: single finger module, particle jamming chamber attached to the finger, and layer jamming chamber attached to the finger. The comparison reveals that combining a soft finger with particle jamming increased performance by 20% compared to using the soft finger alone, while combining it with layer jamming led to an 80% increase. Additionally, layer jamming combined with a soft finger showed a 28% increase compared to particle jamming combined with a soft finger. Furthermore, simulation of the soft finger was conducted to estimate the deflection of the soft gripper finger under various applied forces. Moreover, proposed closed loop smart stiffness mechanism for the soft gripper was modeled and simulated by evaluating both soft and hard objects and simulation results were obtained for different cases. The findings indicated that the stiffness of the soft gripper finger can be adjusted for different grasping requirements.

Gamma oscillation optimally predicts finger movements

Our fingers are the most dexterous and complicated parts of our body and play a significant role in our daily activities. Non-invasive techniques, such as Electroencephalography (EEG) and Electromyography (EMG) can be used to collect neural and muscular signals related to finger movements. In this study, we combined an 8-channel EMG and a 31-channel EEG while the human subject moved one of the five fingers on the right hand. To identify the best EEG frequency features that encode distinct finger movements, we systematically examined the decoding accuracies of the slow-cortical potentials and three types of sensorimotor rhythms, namely the Mu, beta, and gamma oscillations. For both EMG and EEG, we came up with a simple and unified root mean square or power approach that avoided the complex signal features used by previous studies. The signal features were then fed into a feedforward artificial-neural-network (ANN) classifier. We found that the low-gamma oscillation provided the best decoding performance over the other frequency bands, ranging from 65.0 % to 89.0 %, which was comparable to the EMG performance. Combining EMG and low gamma into a single ANN can further improve the outcome for subjects who had showed suboptimal performances with EMG or EEG alone. This study provided a simple and efficient algorithm for prosthetics that assist patients with sensorimotor impairments.

Cortical areas for planning sequences before and during movement.

Production of rapid movement sequences relies on preparation before (pre-planning) and during (online planning) movement. Here, we compared these processes and asked whether they recruit different cortical areas. Human participants performed three single-finger and three multi-finger sequences in a delayed movement paradigm while undergoing 7T functional MRI. During preparation, primary motor (M1) and somatosensory (S1) areas showed pre-activation of the first movement, even without increases in overall activation. During production, the temporal summation of activity patterns corresponding to constituent fingers explained activity in these areas (M1 and S1). In contrast, the dorsal premotor cortex (PMd) and anterior superior parietal lobule (aSPL) showed substantial activation during the preparation (pre-planning) of multi-finger compared to single-finger sequences. These regions (PMd and aSPL) were also more active during production of multi-finger sequences, suggesting that pre- and online planning may recruit the same regions. However, we observed small but robust differences between the two contrasts, suggesting distinct contributions to pre- and online planning. Multivariate analysis revealed sequence-specific representations in both PMd and aSPL, which remained stable across both preparation and production phases. Our analyses show that these areas maintain a sequence-specific representation before and during sequence production, likely guiding the execution-related areas in the production of rapid movement sequences.Significance Statement Understanding how the brain orchestrates complex behavior remains a core challenge in human neuroscience. Here, we combine high-resolution neuroimaging and a carefully crafted design to study the neural control of rapid sequential finger movements, like typing or playing the piano. Advancing prior research, we show that the brain areas involved in planning these movements maintain those representations throughout the execution of the sequence. This representational stability across planning and execution suggests an intricate connection between these processes. Our results shed light on the nuanced contributions of different cortical areas to different aspects of coordinating skilled movement. This work is well placed to inform future research in animal models and the development of targeted interventions against movement disorders.

IOT Gesture Recognition-Based Virtual Keyboard and Mouse

Recognition-based virtual mouse and keyboard systems use gesture recognition technology to control mouse movements and keyboard inputs without physical devices. Instead of using a physical mouse or keyboard, users can perform hand or finger gestures in the air, which are captured and interpreted by a camera or sensor system. Gesture abstracts in this context refer to the specific hand or finger movements that are recognized and associated with certain mouse or keyboard actions. These gestures are predefined and programmed into the recognition system, allowing users to perform specific movements to simulate the actions of a traditional mouse or keyboard

NCMP-23. PATIENTS WITH BRAIN TUMORS HAVE DECREASED BILATERAL FINGER DEXTERITY COMPARED TO HEALTHY ADULTS

Abstract Unilateral brain tumors are known to affect motor control of the contralateral hemibody, but ipsilesional effects are less frequently studied and may be just as impactful to quality of life. Hand dexterity, for example, is a critical skill that often requires both hands. Here, a cohort of 15 patients with unilateral brain tumors and 47 healthy adults performed individual finger movements with each finger (except the thumb) on each hand while tracked by a data glove (Cyberglove III, CyberGlove Systems, San Jose, CA). The ability to move each finger independently was quantified using a scale previously developed by our lab (individuation score) that represents dexterous ability. Repeated-measures two-way ANOVA with factors of group (brain tumor vs. healthy) and laterality (contralesional vs. ipsilesional) were performed for each finger. The lesional side was set to the left brain for cases in the brain tumor group for simplicity of analysis. There was a significant main effect of “group” for all finger individuation scores (index p-value&amp;lt;0.0001, middle p-value&amp;lt;0.0001, ring p-value&amp;lt;0.001, small p-value&amp;lt;0.01) with healthy volunteers scoring higher than patients. There was no main effect of “laterality.” Group-laterality interaction effects were not significant except for the small finger where healthy adults’ right but not left sided individuation scores were greater than the brain tumor group’s (p-value&amp;lt;0.01). Additionally, using cluster analysis incorporating data from both hands, we were able to differentiate patients with brain tumors from healthy adults with 97.9% specificity, 60% sensitivity, 90% positive predictive value, and 88.5% negative predictive value. Ultimately, these results suggest that unilateral brain tumors decrease hand dexterity bilaterally without a significant lateralizing difference except in the small finger. These results suggest unilateral lesions can have bihemispheric network effects, and/or that hand dexterity may rely on ipsilesional connections more than previously understood.

Arthroscopic Management of Metacarpophalangeal Joint Synovial Chondromatosis

AbstractSynovial chondromatosis is a rare condition where cartilaginous nodules form within the synovium, which can detach and cause loose bodies within the joint. While it typically affects larger joints, involvement of the metacarpophalangeal (MCP) joints is rarely described. We report the case of a 42-year-old female patient who had a slow-growing mass in her right third MCP (MCP III) joint, associated with limited finger motion, swelling, and pain. The diagnosis was confirmed using imaging studies. The patient underwent arthroscopic excision of multiple tumors and synovectomy via dorsal radial and ulnar MCP III portals. Postoperatively, there was rapid improvement in hand function, including normal and pain-free range of motion. This case highlights the benefits of arthroscopic management for synovial chondromatosis in the MCP joint, as it offers advantages like minimal invasiveness, reduced tissue damage, faster recovery times, and lower infection risk compared with traditional open methods.

Seeing the piles of the velvet bending under our finger sliding over a tactile stimulator improves the feeling of the fabric.

Using friction modulation to simulate fabrics with a tactile stimulator (i.e. virtual surface) is not sufficient to render fabric touch and even more so for hairy fabrics. We hypothesized that seeing the pile of the velvet darken or lighten depending on changes in the finger movement direction on the virtual surface should improve the velvet fabric rendering. Participants actively rubbed a tactile device or a velvet fabric looking at a screen that showed a synthesized image of a velvet that either remained static (V-static) or darkening/lightening with the direction of touch (V-moving). We showed that in V-moving condition, the touched surface was always perceived rougher, which is a descriptor of a real velvet (Experiment 1). Using electroencephalography and sources localization analyses, we found increased activity in the occipital and inferior parietal lobes (Experiment 2) when seeing dark and shining traces during back-and-forth finger movements over the virtual surface. This suggests that these two posterior cortical regions work together to evaluate visuo-tactile congruence between the seen and the felt (tactile). The visuo-tactile binding, evidenced by neural synchronization (specifically, theta band (5-7 Hz) oscillation) in the left inferior posterior parietal lobule, is consistent with enhanced integration of information and probably contributed to the emergence of a more realistic velvet representation.

Tubercular dactylitis/Spina ventosa-A rare skeletal form of extrapulmonary tuberculosis.

A 9-year-old girl presented with progressive, painful swelling over the left index finger, associated with local signs of inflammation and restriction of finger movements. After the swelling was punctured using a needle, chronic discharging sinus formed. Examination revealed firm swelling over the right parotid area, bilateral cervical and left axillary lymphadenopathy, and firm swelling over the left index finger with a chronic discharging sinus over the lateral aspect of proximal phalanx. Mantoux intradermal injection was reactive (22x24 mm induration). Blood and pus cultures were sterile. Hand radiograph revealed dactylitis (Figure 1A-C). Fine aspiration and cytology of the cervical lymph node showed degenerated inflammatory cells, epithelioid granuloma and acid-fast bacilli. She was initiated on 4-drug antitubercular therapy (ATT). At 2-months follow-up, she was asymptomatic and had the pain and swelling over the left finger have reduced with significant radiological improvement. Tubercular dactylitis or 'spina ventosa' (wind-filled sail) refers to cystic expansion of short, tubular bones. It is commonly seen in younger children (between 1 and 6 years of age) and adults (between 20 and 50 years of age). A common presentation in endemic areas is presence of a painless, fusiform swelling over a single digit, most commonly the proximal phalanx of index or middle finger as in the index case, or over the metacarpals of middle and ring fingers. Earliest radiological clues include periosteitis followed by gradual destruction of bone and cyst formation in the cavity giving a ballooned-out appearance.

Movement Strategy Moderates the Effect of Spatially Congruent Cues on the Stability of Rhythmic Bimanual Finger Movements.

Spatially congruent cues increase the speed of bimanual reach decisions compared with abstract symbolic cues, particularly for asymmetric reaches. Asymmetric rhythmic bimanual movements are less stable than symmetric rhythmic movements, but it is not well understood if spatially congruent cues similarly increase the stability of asymmetric rhythmic bimanual movements. To address this question, in Experiment 1, participants performed symmetric and asymmetric bimanual rhythmic finger tapping movements at different movement frequencies in time with flickering spatially congruent and abstract symbolic stimuli. As expected, symmetric movements were more stable. Spatially congruent cues similarly increased the stability of symmetric and asymmetric movements compared with abstract symbolic cues. The benefits of spatial congruence and movement symmetry were restricted to high movement frequencies (>2 Hz). To better understand if the emergence of these effects at high movement frequencies was driven by a change in movement strategy, in Experiment 2, video of the hands was concurrently recorded during task performance. Markerless motion tracking software revealed that participants switched from discontinuous to continuous movement strategies with increasing movement frequency. Because discontinuous and continuous movements are thought to be controlled by distinct neurocognitive systems, this might explain why the beneficial effects of spatial congruence and response symmetry emerged only at high movement frequencies. Overall, results from the current study indicate that the perceptual quality of the stimulus use to cue movement frequency can have powerful effects on the stability of rhythmic bimanual movements, but that these effects may depend on whether discontinuous or continuous movement strategies are selected.

Sensors for Smoking Detection in Epidemiological Research: Scoping Review.

The use of wearable sensors is being explored as a challenging way to accurately identify smoking behaviors by measuring physiological and environmental factors in real-life settings. Although they hold potential benefits for aiding smoking cessation, no single wearable device currently achieves high accuracy in detecting smoking events. Furthermore, it is crucial to emphasize that this area of study is dynamic and requires ongoing updates. This scoping review aims to map the scientific literature for identifying the main sensors developed or used for tobacco smoke detection, with a specific focus on wearable sensors, as well as describe their key features and categorize them by type. According to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) protocol, an electronic search was conducted on the PubMed, MEDLINE, and Web of Science databases, using the following keywords: ("biosensors" OR "biosensor" OR "sensors" OR "sensor" OR "wearable") AND ("smoking" OR "smoke"). Among a total of 37 studies included in this scoping review published between 2012 and March 2024, 16 described sensors based on wearable bands, 15 described multisensory systems, and 6 described other strategies to detect tobacco smoke exposure. Included studies provided details about the design or application of wearable sensors based on an elastic band to detect different aspects of tobacco smoke exposure (eg, arm, wrist, and finger movements, and lighting events). Some studies proposed a system composed of different sensor modalities (eg, Personal Automatic Cigarette Tracker [PACT], PACT 2.0, and AutoSense). Our scoping review has revealed both the obstacles and opportunities linked to wearable devices, offering valuable insights for future research initiatives. Tackling the recognized challenges and delving into potential avenues for enhancement could elevate wearable devices into even more effective tools for aiding smoking cessation. In this context, continuous research is essential to fine-tune and optimize these devices, guaranteeing their practicality and reliability in real-world applications.