Artificial neural network trained on smartphone behavior can trace epileptiform activity in epilepsy

Age-related behavioral resilience in smartphone touchscreen interaction dynamics

We experience a life that is full of ups and downs. The ability to bounce back after adverse life events such as the loss of a loved one or serious illness declines with age, and such isolated events can even trigger accelerated aging. How humans respond to common day-to-day perturbations is less clear. Here, we infer the aging status from smartphone behavior by using a decision tree regression model trained to accurately estimate the chronological age based on the dynamics of touchscreen interactions. Individuals (N = 280, 21 to 87 y of age) expressed smartphone behavior that appeared younger on certain days and older on other days through the observation period that lasted up to ~4 y. We captured the essence of these fluctuations by leveraging the mathematical concept of critical transitions and tipping points in complex systems. In most individuals, we find one or more alternative stable aging states separated by tipping points. The older the individual, the lower the resilience to forces that push the behavior across the tipping point into an older state. Traditional accounts of aging based on sparse longitudinal data spanning decades suggest a gradual behavioral decline with age. Taken together with our current results, we propose that the gradual age-related changes are interleaved with more complex dynamics at shorter timescales where the same individual may navigate distinct behavioral aging states from one day to the next. Real-world behavioral data modeled as a complex system can transform how we view and study aging.

Common multi-day rhythms in smartphone behavior

The idea that abnormal human activities follow multi-day rhythms is found in ancient beliefs on the moon to modern clinical observations in epilepsy and mood disorders. To explore multi-day rhythms in healthy human behavior our analysis includes over 300 million smartphone touchscreen interactions logging up to 2 years of day-to-day activities (N401 subjects). At the level of each individual, we find a complex expression of multi-day rhythms where the rhythms occur scattered across diverse smartphone behaviors. With non-negative matrix factorization, we extract the scattered rhythms to reveal periods ranging from 7 to 52 days - cutting across age and gender. The rhythms are likely free-running - instead of being ubiquitously driven by the moon - as they did not show broad population-level synchronization even though the sampled population lived in northern Europe. We propose that multi-day rhythms are a common trait, but their consequences are uniquely experienced in day-to-day behavior.

A model of healthy aging based on smartphone interactions reveals advanced behavioral age in neurological disease

Smartphones offer unique opportunities to trace the convoluted behavioral patterns accompanying healthy aging. Here we captured smartphone touchscreen interactions from a healthy population (N = 684, ∼309 million interactions) spanning 16 to 86 years of age and trained a decision tree regression model to estimate chronological age based on the interactions. The interactions were clustered according to their next interval dynamics to quantify diverse smartphone behaviors. The regression model well-estimated the chronological age in health (mean absolute error = 6 years, R2 = 0.8). We next deployed this model on a population of stroke survivors (N = 41) to find larger prediction errors such that the estimated age was advanced by 6 years. A similar pattern was observed in people with epilepsy (N = 51), with prediction errors advanced by 10 years. The smartphone behavioral model trained in health can be used to study altered aging in neurological diseases.

Temporal clusters of age-related behavioral alterations captured in smartphone touchscreen interactions

Smartphones touchscreen interactions may help resolve if and how real-world behavioral dynamics are shaped by aging. Here, in a sample spanning the adult life span (16 to 86 years, N = 598, accumulating 355 million interactions), we clustered the smartphone interactions according to their next inter-touch interval dynamics. There were age-related behavioral losses at the clusters occupying short intervals (∼100 ms, R² ∼ 0.8) but gains at the long intervals (∼4 s, R² ∼ 0.4). Our approach revealed a sophisticated form of behavioral aging where individuals simultaneously demonstrated accelerated aging in one behavioral cluster versus a deceleration in another. Contrary to the common notion of a simple behavioral decline with age based on conventional cognitive tests, we show that the nature of aging systematically varies according to the underlying dynamics. Of all the imaginable factors determining smartphone interactions, age-sensitive cognitive and behavioral processes may dominatingly shape smartphone dynamics.

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The timing of smartphone touchscreen interactions varies from one person to the next

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A diverse range of interactions was studied by quantifying the next interval dynamics

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The smartphone dynamics reflected the performance in cognitive tests and age

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The way aging shapes behavior may depend on its underlying temporal dynamics

Neuromechanical Biomarkers for Robotic Neurorehabilitation

One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.