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1. Age-dependent differences in learning to control a robot arm using a body-machine interface

   https://doi.org/10.1038/s41598-018-38092-3  

   Ranganathan, Rajiv; Lee, Mei-Hua; Padmanabhan, Malavika R.; Aspelund, Sanders; Kagerer, Florian A.; Mukherjee, Ranjan (February 2019, Scientific Reports)

   Abstract Body-machine interfaces, i.e. interfaces that rely on body movements to control external assistive devices, have been proposed as a safe and robust means of achieving movement and mobility; however, how children learn these novel interfaces is poorly understood. Here we characterized the learning of a body-machine interface in young unimpaired adults, two groups of typically developing children (9-year and 12-year olds), and one child with congenital limb deficiency. Participants had to control the end-effector of a robot arm in 2D using movements of the shoulder and torso. Results showed a striking effect of age - children had much greater difficulty in learning the task compared to adults, with a majority of the 9-year old group unable to even complete the task. The 12-year olds also showed poorer task performance compared to adults (as measured by longer movement times and greater path lengths), which were associated with less effective search strategies. The child with congenital limb deficiency showed superior task performance compared to age-matched children, but had qualitatively distinct coordination strategies from the adults. Taken together, these results imply that children have difficulty learning non-intuitive interfaces and that the design of body-machine interfaces should account for these differences in pediatric populations. 

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2. Children show limited movement repertoire when learning a novel motor skill

   Lee, Mei-Hua; Farshchiansadegh, Ali; Ranganathan, Rajiv (September 2017, Developmental science)

   Examining age differences in motor learning using real-world tasks is often problematic due to task novelty and biomechanical confounds. Here, we investigated how children and adults acquire a novel motor skill in a virtual environment. Participants of three different age groups (9-year-olds, 12-year-olds, and adults) learned to use their upper body movements to control a cursor on a computer screen. Results showed that 9-year-old and 12-year-old children showed poorer ability to control the cursor at the end of practice. Critically, when we investigated the movement coordination, we found that the lower task performance of children was associated with limited exploration of their movement repertoire. These results reveal the critical role of motor exploration in understanding developmental differences in motor learning. 

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3. Surface electromyography evaluation for decoding hand motor intent in children with congenital upper limb deficiency

   https://doi.org/10.1038/s41598-024-82519-z  

   Battraw, Marcus_A; Fitzgerald, Justin; Winslow, Eden_J; James, Michelle_A; Bagley, Anita_M; Joiner, Wilsaan_M; Schofield, Jonathon_S (December 2024, Scientific Reports)

   Abstract Children born with congenital upper limb absence exhibit consistent and distinguishable levels of biological control over their affected muscles, assessed through surface electromyography (sEMG). This represents a significant advancement in determining how these children might utilize sEMG-controlled dexterous prostheses. Despite this potential, the efficacy of employing conventional sEMG classification techniques for children born with upper limb absence is uncertain, as these techniques have been optimized for adults with acquired amputations. Tuning sEMG classification algorithms for this population is crucial for facilitating the successful translation of dexterous prostheses. To support this effort, we collected sEMG data from a cohort of N = 9 children with unilateral congenital below-elbow deficiency as they attempted 11 hand movements, including rest. Five classification algorithms were used to decode motor intent, tuned with features from the time, frequency, and time–frequency domains. We derived the congenital feature set (CFS) from the participant-specific tuned feature sets, which exhibited generalizability across our cohort. The CFS offline classification accuracy across participants was 73.8% ± 13.8% for the 11 hand movements and increased to 96.5% ± 6.6% when focusing on a reduced set of five movements. These results highlight the potential efficacy of individuals born with upper limb absence to control dexterous prostheses through sEMG interfaces. 

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4. Understanding the capacity of children with congenital unilateral below-elbow deficiency to actuate their affected muscles

   https://doi.org/10.1038/s41598-024-54952-7  

   Battraw, Marcus A; Fitzgerald, Justin; James, Michelle A; Bagley, Anita M; Joiner, Wilsaan M; Schofield, Jonathon S (December 2024, Scientific Reports)

   Abstract In recent years, commercially available dexterous upper limb prostheses for children have begun to emerge. These devices derive control signals from surface electromyography (measure of affected muscle electrical activity, sEMG) to drive a variety of grasping motions. However, the ability for children with congenital upper limb deficiency to actuate their affected muscles to achieve naturalistic prosthetic control is not well understood, as compared to adults or children with acquired hand loss. To address this gap, we collected sEMG data from 9 congenital one-handed participants ages 8–20 years as they envisioned and attempted to perform 10 different movements with their missing hands. Seven sEMG electrodes were adhered circumferentially around the participant’s affected and unaffected limbs and participants mirrored the attempted missing hand motions with their intact side. To analyze the collected sEMG data, we used time and frequency domain analyses. We found that for the majority of participants, attempted hand movements produced detectable and consistent muscle activity, and the capacity to achieve this was not dissimilar across the affected and unaffected sides. These data suggest that children with congenital hand absence retain a degree of control over their affected muscles, which has important implications for translating and refining advanced prosthetic control technologies for children. 

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5. Left digit bias in children’s and adults’ paper-and-pencil number line estimation

   https://doi.org/10.3758/s13421-025-01707-y  

   Barth, Hilary; Rutkowski, Bethany; Vaidya, Leah; Kim, Erin; Bourassa, Cameron; Fabian, Annie; Eisen, Sierra; Zax, Alexandra; Williams, Katherine; Patalano, Andrea L (April 2025, Memory & Cognition)

   Number line estimation tasks are frequently used to learn about numerical thinking, learning, and development. These tasks are often interpreted as though estimates are determined by overall magnitudes of target numerals, rather than specific instantiating digits. Yet estimates are strongly biased by leftmost digits. For example, numbers like “698” are placed too far to the left of numbers like “701” on a 0–1,000 line. This “left digit effect” or “left digit bias” has been investigated little in children, and only on electronic tasks. Here, we ask whether left digit bias appears in paper-and-pencil estimates, and whether it differs for paper-based versus computer-based tasks. In Study 1, 5- to 8-year-old children completed a 0–100 number line task on paper. In Study 2, 7- to 11-year-olds completed a 0–1,000 paper task. In Study 3, adults completed tasks on paper in both ranges. Large left digit effects were observed for children aged 8 years or older and adults, but we did not find evidence for left digit bias in younger children. Study 4 compared paper and computer tasks for adults and children aged 9–12 years. Strong left digit bias was observed in all conditions, with a larger effect for the paper-based task in children. Large left digit effects in number line estimation emerge regardless of task format, with a developmental trajectory broadly consistent with other studies. For children in the age range that reliably exhibits left digit bias (but not adults), paper-and-pencil number line estimation tasks elicit even greater bias than computer-based tasks. 

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