scholarly journals Making Sense of Complex Sensor Data Streams

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1391
Author(s):  
Rongrong Liu ◽  
Birgitta Dresp-Langley
Keyword(s):  
Data Analysis ◽  
Grip Force ◽  
Human Performance ◽  
Precision Grip ◽  
Individual Variability ◽  
Sensor Data ◽  
Dominant Hand ◽  
Analysis Strategies ◽  
The Individual ◽  
Force Profiles

This concept paper draws from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non-dominant hand of operators performing a robot-assisted precision grip task for minimally invasive endoscopic surgery. The specificity of the robotic system on the one hand, and that of the 2D image-guided task performed in a real-world 3D space on the other, constrain the individual hand and finger movements during task performance in a unique way. Our previous work showed task-specific characteristics of operator expertise in terms of specific grip force profiles, which we were able to detect in thousands of highly variable individual data. This concept paper is focused on two complementary data analysis strategies that allow achieving such a goal. In contrast with other sensor data analysis strategies aimed at minimizing variance in the data, it is necessary to decipher the meaning of intra- and inter-individual variance in the sensor data on the basis of appropriate statistical analyses, as shown in the first part of this paper. Then, it is explained how the computation of individual spatio-temporal grip force profiles allows detecting expertise-specific differences between individual users. It is concluded that both analytic strategies are complementary and enable drawing meaning from thousands of biosensor data reflecting human performance measures while fully taking into account their considerable inter- and intra-individual variability.

scholarly journals Correlating Grip Force Signals from Multiple Sensors Highlights Prehensile Control Strategies in a Complex Task-User System

2020 ◽  
Vol 7 (4) ◽  
pp. 143
Author(s):  
Birgitta Dresp-Langley ◽  
Florent Nageotte ◽  
Philippe Zanne ◽  
Michel de Mathelin
Keyword(s):  
Real Time ◽  
Force Control ◽  
Grip Force ◽  
Control Strategies ◽  
Sensor Data ◽  
Dominant Hand ◽  
Complex Task ◽  
The Individual ◽  
Receptive Field Organization ◽  
Force Profiles

Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is generally wireless and enables the non-invasive monitoring of signals to track and trace user behaviors in real time. Examples include signals relative to hand and finger movements or force control reflected by individual grip force data. As will be shown here, these signals directly translate into task, skill, and hand-specific (dominant versus non-dominant hand) grip force profiles for different measurement loci in the fingers and palm of the hand. The present study draws from thousands of such sensor data recorded from multiple spatial locations. The individual grip force profiles of a highly proficient left-hander (expert), a right-handed dominant-hand-trained user, and a right-handed novice performing an image-guided, robot-assisted precision task with the dominant or the non-dominant hand are analyzed. The step-by-step statistical approach follows Tukey’s “detective work” principle, guided by explicit functional assumptions relating to somatosensory receptive field organization in the human brain. Correlation analyses (Person’s product moment) reveal skill-specific differences in co-variation patterns in the individual grip force profiles. These can be functionally mapped to from-global-to-local coding principles in the brain networks that govern grip force control and its optimization with a specific task expertise. Implications for the real-time monitoring of grip forces and performance training in complex task-user systems are brought forward.

scholarly journals Wearable Wireless Biosensors for Spatiotemporal Grip Force Profiling in Real Time

2020 ◽  
Vol 2 (1) ◽  
pp. 45
Author(s):  
Rongrong Liu ◽  
Florent Nageotte ◽  
Philippe Zanne ◽  
Michel de Mathelin ◽  
Birgitta Dresp-Langley
Keyword(s):  
Real Time ◽  
Grip Force ◽  
Behavioral Science ◽  
Middle Finger ◽  
Sensor Data ◽  
Dominant Hand ◽  
Biologically Inspired ◽  
Systems Neuroscience ◽  
Spatio Temporal ◽  
Force Profiles

The temporal evolution of individual grip force profiles of a novice using a robotic system for minimally invasive endoscopic surgery is analyzed on the basis of thousands of individual sensor data recorded in real time through a wearable wireless sensor glove system. The spatio-temporal grip force profiles from specific sensor locations in the dominant hand performing a four-step pick-and-drop simulator task reveal skill-relevant differences in force deployment by the small finger (fine grip force control) and the middle finger (gross grip force contribution) by comparison with the profiles of a highly proficient expert. Cross-disciplinary insights from systems neuroscience, cognitive behavioral science, and robotics, with implications for biologically inspired AI for human–robot interactions, highlight the functional significance of spatio-temporal grip force profiling.

scholarly journals Correlating Grip Force Signals From Multiple Sensors Highlights Functional Synergies of Manual Control in a Complex Task-User System

2020 ◽  
Author(s):  
Birgitta Dresp-Langley ◽  
Florent Nageotte ◽  
Philippe Zanne ◽  
Michel de Mathelin
Keyword(s):  
Real Time ◽  
Grip Force ◽  
Pressure Control ◽  
Sensor Data ◽  
Dominant Hand ◽  
Complex Task ◽  
Multiple Sources ◽  
Multiple Sensors ◽  
Palm Of The Hand ◽  
Force Profiles

Biosensors and wearable sensor systems with transmitting capabilities are currently developed and used for the monitoring of health data, exercise activities, and other performance data. Unlike conventional approaches, these devices enable convenient, continuous, and unobtrusive monitoring of a user’s behavioral signals in real time. Examples include signals relative to hand an finger movement/pressure control reflected by individual grip force data. As will be shown here, these directly translate into task, skill and hand-specific (dominant versus non-dominant hand) grip force profiles for different measurement loci in the fingers and palm of the hand. On the basis of thousands of sensor data from multiple sensor locations, individual grip force profiles of an task expert, a trained user and a highly proficient user (expert) performing an image-guided and robot-assisted precision task with the dominant or the non-dominant hand are analyzed in several steps following Tukey’s “detective work” approach. Correlation analyses (Person’s Product Moment) reveal skill-specific differences in individual grip force profiles across multiple sources of variation, functionally mapped to the somatosensory brain networks which ensure grip force control and its evolution with control expertise. Implications for the real-time monitoring of individual grip force profiles and their evolution with training in complex task-user systems are brought forward.

scholarly journals Variability of manual dexterity performance in non-human primates (Macaca fascicularis

2014 ◽  
Vol 27 (2) ◽  
Author(s):  
Mélanie Kaeser ◽  
Pauline Chatagny ◽  
Anne-Dominique Gindrat ◽  
Julie Savidan ◽  
Simon Badoud ◽  
...  
Keyword(s):  
Motor Learning ◽  
Macaca Fascicularis ◽  
Precision Grip ◽  
Individual Variability ◽  
Manual Dexterity ◽  
Learning Phase ◽  
Load Force ◽  
Dominant Hand ◽  
Reach And Grasp ◽  
Initial Score

The goal of this study was to quantify the inter-individual and intra-individual variability of manual (digits) skill in adult macaque monkeys, over a motor learning phase and, lateron, when motor skills were consolidated. The hypothesis is that several attributes of the stable manual dexterity performance can be predicted from learning characteristics. The behavioral data were collected from 20 adult Macaca fascicularis, derived from their dominant hand, defined as the hand exhibiting a better performance than theother. Two manual dexterity tasks were tested: (i) the modified Brinkman board task, consisting in the retrieval of food pellets placed in 50 slots ina board, using the precision grip (opposition of the thumb and index finger);(ii) the reach and grasp drawer task, in which the grip force and the load force were continuously monitored while the monkey opened a drawer against a resistance, before grasping a pellet inside the drawer. The hypothesis was verified for the performance of manual dexterity after consolidation, correlated with the initial score before learning. Motor habit, reflected by the temporal order of sequential movements executed in the modified Brinkman board task, was established very early during the learning phase. As mostly expected, motor learning led to an optimization of manual dexterity parameters, such as score, contact time, as well as a decrease in intra-individual variability. Overall,the data demonstrate the substantial inter-individual variability of manual dexterity in non-human primates, to be considered for further pre-clinical applications based on this animal model.

THE STUDY OF THE INDIVIDUAL VARIABILITY OF THE LINEAR DIMENSIONS OF THE SPHENOID SINUS

2019 ◽  
Vol 21 (1) ◽  
pp. 64-70
Author(s):  
O.V. Mareev ◽  
◽  
G.O. Mareev ◽  
M.E. Gutynina ◽  
D.A. Maksimova ◽  
...  
Keyword(s):  
Sphenoid Sinus ◽  
Individual Variability ◽  
Linear Dimensions ◽  
The Individual

Visual and Tactile Information About Object-Curvature Control Fingertip Forces and Grasp Kinematics in Human Dexterous Manipulation

2000 ◽  
Vol 84 (6) ◽  
pp. 2984-2997 ◽  
Author(s):  
Per Jenmalm ◽  
Seth Dahlstedt ◽  
Roland S. Johansson
Keyword(s):  
Visual Processing ◽  
Visual Cues ◽  
Grip Force ◽  
Center Of Mass ◽  
Precision Grip ◽  
Sensory Information ◽  
Afferent Input ◽  
Dependent Manner ◽  
Tactile Sensibility ◽  
Grasp Stability

Most objects that we manipulate have curved surfaces. We have analyzed how subjects during a prototypical manipulatory task use visual and tactile sensory information for adapting fingertip actions to changes in object curvature. Subjects grasped an elongated object at one end using a precision grip and lifted it while instructed to keep it level. The principal load of the grasp was tangential torque due to the location of the center of mass of the object in relation to the horizontal grip axis joining the centers of the opposing grasp surfaces. The curvature strongly influenced the grip forces required to prevent rotational slips. Likewise the curvature influenced the rotational yield of the grasp that developed under the tangential torque load due to the viscoelastic properties of the fingertip pulps. Subjects scaled the grip forces parametrically with object curvature for grasp stability. Moreover in a curvature-dependent manner, subjects twisted the grasp around the grip axis by a radial flexion of the wrist to keep the desired object orientation despite the rotational yield. To adapt these fingertip actions to object curvature, subjects could use both vision and tactile sensibility integrated with predictive control. During combined blindfolding and digital anesthesia, however, the motor output failed to predict the consequences of the prevailing curvature. Subjects used vision to identify the curvature for efficient feedforward retrieval of grip force requirements before executing the motor commands. Digital anesthesia caused little impairment of grip force control when subjects had vision available, but the adaptation of the twist became delayed. Visual cues about the form of the grasp surface obtained before contact was used to scale the grip force, whereas the scaling of the twist depended on visual cues related to object movement. Thus subjects apparently relied on different visuomotor mechanisms for adaptation of grip force and grasp kinematics. In contrast, blindfolded subjects used tactile cues about the prevailing curvature obtained after contact with the object for feedforward adaptation of both grip force and twist. We conclude that humans use both vision and tactile sensibility for feedforward parametric adaptation of grip forces and grasp kinematics to object curvature. Normal control of the twist action, however, requires digital afferent input, and different visuomotor mechanisms support the control of the grasp twist and the grip force. This differential use of vision may have a bearing to the two-stream model of human visual processing.

scholarly journals Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 634
Author(s):  
Tarek Frahi ◽  
Francisco Chinesta ◽  
Antonio Falcó ◽  
Alberto Badias ◽  
Elias Cueto ◽  
...  
Keyword(s):  
Data Analysis ◽  
The State ◽  
Sensor Data ◽  
Topological Data Analysis ◽  
Motion Sensors ◽  
Driver Assistance Systems ◽  
The Road ◽  
Topological Features ◽  
Recent Developments ◽  
Topological Data

We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.

GAMBARAN TINGKAT KECEMASAN ORANG TUA TERHADAP HOSPITALISASI ANAK DENGAN KEJANG DEMAM SELAMA ANAK DI RAWAT DI RUMAH SAKIT SANTA ELISABETH MEDAN TAHUN 2017

2017 ◽  
Vol 2 (2) ◽  
pp. 53-60
Author(s):  
Melky Rismando Damanik ◽  
Rusmauli Lumban Gaol
Keyword(s):  
Data Analysis ◽  
Febrile Seizure ◽  
Febrile Seizures ◽  
Anxiety Level ◽  
Parental Anxiety ◽  
Subjective Experiences ◽  
High Anxiety ◽  
Specific Object ◽  
Anxiety Levels ◽  
The Individual

Anxiety is an unclear and widespread concern, associated with feelings of uncertainty and helplessness. This state of anxiety and emotion has no specific object but can affect behavior toward parents whose children are hospitalized. Parental anxiety levels are subjective experiences of the individual and can not be directly observed but consequently will affect the anxiety level of the parent. Hospitalization of children is a state of crisis in children, when children are sick and hospitalized, one of them in the febrile seizure disease is a seizure spasm that occurs in the rise in body temperature above 38 ° C this will result in anxiety level of parents increases. Goals: To know the description of anxiety level of parent to hospitalization of child with febrile seizure during child is treated in hospital of Elisabeth Elisabeth Medan. Method: The design used in this study is descriptive to describe the level of anxiety parents to hospitalization of children with febrile seizures during child care at Hospital Santa Elisabeth Medan Year 2017. Result: based on data collection found 10 respondents where 5 (50%) of respondents who experienced anxiety level in medium category and 5 (50%) respondents have low level anxiety level. Conclusion: Based on the research and data analysis that has been done in accordance with the objectives that have been determined can be concluded that all parents who care for their children in the hospital will experience anxiety level that is 5 respondents (50%) with moderate anxiety level, while 5 others (50% Low anxiety levels and high anxiety levels were not found.

scholarly journals Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2884 ◽  
Author(s):  
Xiaobo Chen ◽  
Cheng Chen ◽  
Yingfeng Cai ◽  
Hai Wang ◽  
Qiaolin Ye
Keyword(s):  
Data Analysis ◽  
Sparse Representation ◽  
Intelligent Transportation Systems ◽  
Missing Values ◽  
Road Traffic ◽  
Linear Representation ◽  
Transportation Systems ◽  
Sensor Data ◽  
L1 Norm ◽  
L2 Norm

The problem of missing values (MVs) in traffic sensor data analysis is universal in current intelligent transportation systems because of various reasons, such as sensor malfunction, transmission failure, etc. Accurate imputation of MVs is the foundation of subsequent data analysis tasks since most analysis algorithms need complete data as input. In this work, a novel MVs imputation approach termed as kernel sparse representation with elastic net regularization (KSR-EN) is developed for reconstructing MVs to facilitate analysis with traffic sensor data. The idea is to represent each sample as a linear combination of other samples due to inherent spatiotemporal correlation, as well as periodicity of daily traffic flow. To discover few yet correlated samples and make full use of the valuable information, a combination of l1-norm and l2-norm is employed to penalize the combination coefficients. Moreover, the linear representation among samples is extended to nonlinear representation by mapping input data space into high-dimensional feature space, which further enhances the recovery performance of our proposed approach. An efficient iterative algorithm is developed for solving KSR-EN model. The proposed method is verified on both an artificially simulated dataset and a public road network traffic sensor data. The results demonstrate the effectiveness of the proposed approach in terms of MVs imputation.

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