Infant posture and movement analysis using a sensor-supported gym with toys

Quantifying interaction with robotic toys in pre-term and full-term infants

Infants born pre-term are at an increased risk for developmental, behavioral, and motor delay and subsequent disability. When these problems are detected early, clinical intervention can be effective at improving functional outcomes. Current methods of early clinical assessment are resource intensive, require extensive training, and do not always capture infants' behavior in natural play environments. We developed the Play and Neuro Development Assessment (PANDA) Gym, an affordable, mechatronic, sensor-based play environment that can be used outside clinical settings to capture infant visual and motor behavior. Using a set of classification codes developed from the literature, we analyzed videos from 24 pre-term and full-term infants as they played with each of three robotic toys designed to elicit different types of interactions-a lion, an orangutan, and an elephant. We manually coded for frequency and duration of toy interactions such as kicking, grasping, touching, and gazing. Pre-term infants gazed at the toys with similar frequency as full-term infants, but infants born full-term physically engaged more frequently and for longer durations with the robotic toys than infants born pre-term. While we showed we could detect differences between full-term and pre-term infants, further work is needed to determine whether differences seen were primarily due to age, developmental delays, or a combination.

Infant movement classification through pressure distribution analysis

BACKGROUND

Aiming at objective early detection of neuromotor disorders such as cerebral palsy, we propose an innovative non-intrusive approach using a pressure sensing device to classify infant general movements. Here we differentiate typical general movement patterns of the "fidgety period" (fidgety movements) vs. the "pre-fidgety period" (writhing movements).

METHODS

Participants (N = 45) were sampled from a typically-developing infant cohort. Multi-modal sensor data, including pressure data from a pressure sensing mat with 1024 sensors, were prospectively recorded for each infant in seven succeeding laboratory sessions in biweekly intervals from 4 to 16 weeks of post-term age. 1776 pressure data snippets, each 5 s long, from the two targeted age periods were taken for movement classification. Each snippet was pre-annotated based on corresponding synchronised video data by human assessors as either fidgety present or absent. Multiple neural network architectures were tested to distinguish the fidgety present vs. fidgety absent classes, including support vector machines, feed-forward networks, convolutional neural networks, and long short-term memory networks.

RESULTS

Here we show that the convolution neural network achieved the highest average classification accuracy (81.4%). By comparing the pros and cons of other methods aiming at automated general movement assessment to the pressure sensing approach, we infer that the proposed approach has a high potential for clinical applications.

CONCLUSIONS

We conclude that the pressure sensing approach has great potential for efficient large-scale motion data acquisition and sharing. This will in return enable improvement of the approach that may prove scalable for daily clinical application for evaluating infant neuromotor functions.

Motor Overflow during Reaching in Infancy: Quantification of Limb Movement Using Inertial Motion Units

Early in life, infants exhibit motor overflow, which can be defined as the generation of involuntary movements accompanying purposeful actions. We present the results of a quantitative study exploring motor overflow in 4-month-old infants. This is the first study quantifying motor overflow with high accuracy and precision provided by Inertial Motion Units. The study aimed to investigate the motor activity across the non-acting limbs during goal-directed action. To this end, we used wearable motion trackers to measure infant motor activity during a baby-gym task designed to capture overflow during reaching movements. The analysis was conducted on the subsample of participants (n = 20), who performed at least four reaches during the task. A series of Granger causality tests revealed that the activity differed depending on the non-acting limb and the type of the reaching movement. Importantly, on average, the non-acting arm preceded the activation of the acting arm. In contrast, the activity of the acting arm was followed by the activation of the legs. This may be caused by their distinct purposes in supporting postural stability and efficiency of movement execution. Finally, our findings demonstrate the utility of wearable motion trackers for precise measurement of infant movement dynamics.

A HAND USE AND GRASP SENSOR SYSTEM IN MONITORING INFANT FINE MOTOR DEVELOPMENT

Objective

To assess the feasibility of a hand use and grasp sensor system in collecting and quantifying fine motor development longitudinally in an infant's home environment.

Design

Cohort study. Researchers made home visits monthly to participating families to collect grasp data from infants using a hand use and grasp sensor.

Setting

Data collection were conducted in each participant's home.

Participants

A convenience sample of 14 typical developmental infants were enrolled from 3 months to 9 months of age. Two infants dropped out. A total of 62 testing sessions involving 12 infants were available for analysis (N=12).

Interventions

At each session, the infant was seated in a standardized infant seat. Each instrumented toy was hung on the hand use and grasp sensor structure, presented for 6 minutes in 3 feedback modes: visual, auditory, and vibratory.

Main Outcome Measures

Infant grasp frequency and duration, peak grasping force, average grasping force, force coefficient of variation, and proportion of bimanual grasps.

Results

A total of 2832 recorded grasp events from 12 infants were analyzed. In linear mixed-effects model analysis, when interacting with each toy, infants’ peak grasp force, average grasp force, and accumulated grasp time all increased significantly with age (all P<.001). Bimanual grasps also occupied an increasingly greater percentage of infants’ total grasps as they grew older (bar toy P<.001, candy toy P=.021).

Conclusions

We observed significant changes in hand use and grasp sensor outcome measures with age that are consistent with maturation of grasp skills. We envision the evolution of hand use and grasp sensor technology into an inexpensive and convenient tool to track infant grasp development for early detection of possible developmental delay and/or cerebral palsy as a supplement to clinical evaluations.