Design and validation of surface-marker clusters for the quantification of joint rotations in general movements in early infancy

Berthouze, Luc; Mayston, Margaret

doi:10.1016/j.jbiomech.2011.01.016

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…However, such a system is costly and challenging to set up, especially when placing a large number of markers on infants’ little bodies. The work of Berthouze and Mayston [ 86 ] used 3 to 4 markers on the shanks of infants in their surface-marker cluster approach for GMA employing 6 cameras. They have placed a relatively small number of markers but still the system is not easily portable and also have high computational complexity which limits their clinical applicability.…”

Section: Discussionmentioning

confidence: 99%

“…However, such type of 3D motion capture systems are costly, challenging to set up, not easily portable, and have high computational complexity which limits their clinical applicability. The work of Berthouze and Mayston [ 86 ] focused on establishing surface-cluster to access general movement especially focusing on the quantification of joint rotations. Self-cut polycarbonate sheet frames to cluster markers (also referred to as cluster) were evaluated during a validation study with soft-body dummy dolls and a case study consisting of 4 typically developing infants.…”

Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

“…The setup time compared to using simple markers and the risk of removing markers through movement was reduced. The authors “suggest that this surface-marker cluster approach makes it possible to fully quantify infants’ general movements” [ 86 ]. Nevertheless, the use of multiple cameras seems to be expensive and more complex in terms of preparation time compared to approaches using one RGB video.…”

Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

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AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Irshad

Nisar

Gouverneur

et al. 2020

Sensors

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General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl’s assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.

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Section: Discussionmentioning

confidence: 99%

Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

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AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Irshad

Nisar

Gouverneur

et al. 2020

Sensors

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“…The development of reach and grip [23], head movement [24], and arm and trunk movement have all been studied using these combinations. Optoelectronic MCS systems have the drawbacks of timeconsuming preparation of the measuring device and the newborn with a high number of required markers [25], infant self-occlusion, and missing data as a result of infant's unforeseen movement [26]. The use of marker clusters in such a system has the benefit of reducing system complexity.…”

Section: Diverse Methods For Assessing Spastic Cerebral Palsymentioning

confidence: 99%

IMU-Based Approach to Detect Spastic Cerebral Palsy in Infants at Early Stages

Sukhadia,

Kamboj

2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Cerebral Palsy (CP) is a non-progressive neurological disorder affecting muscle control in early childhood, leading to permanent alterations in body posture and movement. Early identification is crucial for accurate diagnosis and therapy-based interventions. In recent years, an automated monitoring system has been developed to facilitate the health assessment of infants, enabling early recognition of neurological dysfunctions in high-risk infants. However, the interpretation of these assessments lacks standardization and is subject to examiner bias. OBJECTIVES: Many infants with CP exhibit increased tonic stretch reflexes due to Upper Motor Neuron Syndrome (UMNS), resulting from motor neuron damage that disrupts muscle signalling. METHOD: To detect abnormal muscle reactions, our team employed an Inertial Measurement Unit (IMU) sensor, comprising three tri-axial sensors (accelerometer, gyroscope, magnetometer) that capture movement data continuously and unobtrusively. IMU sensors are compact, cost-effective, and have low processing requirements, requiring attachment to the infant's body to measure inter-body part angles. Our team analyzed muscle activity and posture using IMU sensors, collecting tri-axial data from 43 infants in real-time. Additional factors like age, stride length, and leg length were incorporated into the dataset. RESULTS: Our team has applied various supervised machine learning approaches to predict CP in infants due to the limited dataset size, validating models through k-fold cross-validation. Among the models, Naive Bayes (NB) outperformed Logistic Regression (LR), Decision Tree (DT), Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN), and Support Vector Machine (SVM), achieving an accuracy of 88%. CONCLUSION: This research contributes to the early detection and intervention of CP in infants, potentially improving their long-term outcomes.

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“…System disadvantages are high number of needed markers, invasiveness, time-consuming preparation of the measurement subject and measurement system. While system complexity was reduced with marker clusters [ 18 ], large segments of missing data due to unexpected infant’s movement and self-occlusion remain a problem [ 7 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Infant trunk posture and arm movement assessment using pressure mattress, inertial and magnetic measurement units (IMUs)

Rihar

Mihelj

Pašič

et al. 2014

Journal of NeuroEngineering and Rehabilitation

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BackgroundExisting motor pattern assessment methods, such as digital cameras and optoelectronic systems, suffer from object obstruction and require complex setups. To overcome these drawbacks, this paper presents a novel approach for biomechanical evaluation of newborn motor skills development. Multi-sensor measurement system comprising pressure mattress and IMUs fixed on trunk and arms is proposed and used as alternative to existing methods. Observed advantages seem appealing for the focused field and in general. Combined use of pressure distribution data and kinematic information is important also for posture assessment, ulcer prevention, and non-invasive sleep pattern analysis of adults.MethodsArm kinematic parameters, such as root-mean-square acceleration, spectral arc length of hand velocity profile, including arm workspace surface area, and travelled hand path are obtained with the multi-sensor measurement system and compared to normative motion capture data for evaluation of adequacy. Two IMUs per arm, only one IMU on upper arm, and only one IMU on forearm sensor placement options are studied to assess influence of system configuration on method precision. Combination of pressure mattress and IMU fixed on the trunk is used to measure trunk position (obtained from mat), rotation (from IMUs) and associated movements on surface (from both). Measurement system is first validated on spontaneous arm and trunk movements of a dedicated baby doll having realistic anthropometric characteristics of newborns. Next, parameters of movements in a healthy infant are obtained with pressure mattress, along with trunk and forearm IMU sensors to verify appropriateness of method and parameters.ResultsEvaluation results confirm that full sensor set, comprising pressure mattress and two IMUs per arm is a reliable substitution to optoelectronic systems. Motor pattern parameter errors are under 10% and kinematic estimation error is in range of 2 cm. Although, use of only forearm IMU is not providing best possible kinematic precision, the simplicity of use and still acceptable accuracy are convincing for frequent practical use. Measurements demonstrated system high mobility and usability.ConclusionsStudy results confirm adequacy of the proposed multi-sensor measurement system, indicating its enviable potential for accurate infant trunk posture and arm movement assessment.Electronic supplementary materialThe online version of this article (doi:10.1186/1743-0003-11-133) contains supplementary material, which is available to authorized users.

show abstract

Design and validation of surface-marker clusters for the quantification of joint rotations in general movements in early infancy

Cited by 12 publications

References 13 publications

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

IMU-Based Approach to Detect Spastic Cerebral Palsy in Infants at Early Stages

Infant trunk posture and arm movement assessment using pressure mattress, inertial and magnetic measurement units (IMUs)

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