Technology & Research

The Learning Corp technology

AI for the human brain 

Our approach to artificial intelligence (AI) is tuned to the dynamic and delicate reality of the human brain. We believe that AI, when used for brain science, must be applied transparently, so that results are explainable and reproducible. We also believe in creating a level of accountability and clarity to demonstrate why an AI-based system recommends the actions it takes.

As each patient’s brain learns and grows, so does our AI. AI exposes patients, clinicians, institutions, and healthcare systems to new knowledge on a regular basis, thereby advancing the science of brain recovery in all of its depth and breadth.

Meet the NeuroPerformance Engine

Our patented NeuroPerformance Engine™ is the revolutionary AI at the core of Constant Therapy and all products created by The Learning Corp.

The NeuroPerformance Engine™ constantly monitors and analyzes the real world evidence generated by patient performance and progress. Through self-learning technology, it presents—and constantly adjusts—exercises based on patients’ past performance data, and on the performance data of thousands of other people with similar conditions.

Powered by the NeuroPerformance Engine™, Constant Therapy delivers personalized exercises to patients at the right time, appropriately calibrated to where they are in their therapy and recovery process. Because the NeuroPerformance Engine™ is self-learning and adaptive, its ability to deliver new exercises is limitless. Every 3.5 seconds, a patient completes an exercise, generating the real world evidence that teaches the program something new.

Thanks to the real-time, real world evidence collected by the NeuroPerformance Engine™The Learning Corp discovers ways to improve individual exercises, fine-tune the sequence of therapies, and update current protocols. And because the way in which data is collected rigorously conforms to best practices and existing standards of care, The Learning Corp can advance brain science by publishing and sharing our learnings. Illuminating more effective therapeutic approaches to clinicians, neurologists, and other scientists can help to bring better methods and results to patients all around the world.

Constant Therapy

Constant Therapy is an award-winning cognitive, language, and speech therapy app, powered by the NeuroPerformance Engine™.

By combining cutting-edge AI technology with real world evidence based research, Constant Therapy is advancing brain science and elevating the standard of brain rehabilitation.

Maximizing recovery potential

Constant Therapy delivers personalized, clinically proven therapy to those with brain injury and neurological and speech disorders. It includes exercises across 80 categories of cognitive, language, and speech therapy, and is designed for independent use or in conjunction with clinical therapy.

Constant Therapy can be used for patients with a range of conditions, including stroke, traumatic brain injury (TBI), and neurological disorders such as aphasia, dementia, and Alzheimer’s disease.

Real world evidence-based and clinically proven

  • All exercises in the Constant Therapy app are well established and clinically proven to improve brain function

Personalized and adaptive

  • The NeuroPerformance Engine™ constantly monitors and analyzes patient performance to optimize and individualize their exercises

Maintain clinical therapy after discharge 

  • With unlimited access, patients can maintain clinically proven therapy once in-clinic sessions are no longer covered by insurance
  • Standardizes care and reduces administrative burden

Real-time performance metrics and analytics

  • Providers can view real-time, quantitative performance metrics and analytics to fine-tune and improve the standard of care

Automated reporting and documentation

  • The Constant Therapy platform automates reporting and documentation from every level of care, across institutions: patients, clinicians, and facilities
  • Automated reports and documentation reduce a significant burden from providers, creating process efficiencies, consistencies, and cost savings

Improving access

Constant Therapy is the high-value, low-cost solution to improving access to therapy.

According to an analysis by CostHelper Health, the estimated national average cost of a single speech therapy session is between $100 to $250. For less than the cost of one session, a patient can access a full year of unlimited, on-demand access to personalized, clinically proven cognitive, language, and speech therapy.

Constant Therapy is the answer to maintaining clinical therapy after discharge, so patients can reach their recovery potential. It is simple to implement and available to anyone—putting hope for recovery into as many hands as possible.

New study signals benefits of Constant Therapy for stroke patients

A new study comparing home and clinic Constant Therapy users shows that home users took less time to master tasks than users who only practiced in the clinic, and home users practiced therapy more frequently than clinic users. See the study.

Learn about
Constant Therapy

The Learning Corp research

The Learning Corp, makers of Constant Therapy, is home to one of the world’s largest brain rehabilitation databases – one that continues to grow every day.

The Learning Corp—areas of clinical investigation

Scientific areas of investigation include stroke, TBI, and neurological disorders such as aphasia, dementia, and Alzheimer’s disease.

Constant Therapy—by the numbers

  • Every 3.5 seconds, a patient completes a Constant Therapy exercise
  • Thousands of clinicians recommend Constant Therapy as an integral part of their therapy programs
  • 100,000 Constant Therapy users
  • 100 million+ Constant Therapy exercises completed
  • Limitless exercises can be generated by Constant Therapy, as each are continuously updated and expanded with every year

Current therapy domains addressed by Constant Therapy

Cognition

  • Arithmetic
  • Quantitative reasoning
  • Analytical reasoning
  • Visuospatial processing
  • Auditory memory
  • Visual memory
  • Attention

Speech and Language

  • Writing
  • Reading
  • Speaking
  • Sentence planning
  • Word retrieval
  • Auditory comprehension
  • Phonological processing

Clinical research using Constant Therapy

In a number of controlled clinical trials using Constant Therapy, compelling results demonstrate the benefit of using standardized and personalized therapy on a digital platform.

View Research using Constant Therapy
  1. Godlove, J., Anantha, V., Advani, M., Des Roches, C., Kiran, S. Comparison of therapy practice at home and in the clinic: A retrospective analysis of the Constant Therapy platform data set. Frontiers in Neurology. doi: 10.3389/fneur.2019.00140
  2. Kiran S, Godlove J, Advani M, Anantha V. Personalizing rehabilitation for stroke survivors: a big data approach. Presented at: International Stroke Conference; February 22-24, 2017; Houston, TX.
  3. Des Roches CA, Mitko A, Kiran S. Relationship between self-administered cues and rehabilitation outcomes in individuals with aphasia: understanding individual responsiveness to a technology-based rehabilitation program. Front Hum Neurosci. 2017;11:07. doi:10.3389/fnhum.2017.00007.
  4. Mallet KH, Shamloul RM, Corbett D, et al. RecoverNow: Feasibility of a mobile tablet-based rehabilitation intervention to treat post-stroke communication deficits in the acute care setting. PLoS One. 2016;11(12):e0167950. doi:10.1371/journal.pone.0167950.
  5. Glynn P, Eom S, Zelko F, Koh S. Feasibility of a mobile cognitive intervention in childhood absence epilepsy. Front Hum Neurosci. 2016;10:575. doi:10.3389/fnhum.2016.00575.
  6. Postman WA. Computer-mediated cognitive-communicative intervention for residents with dementia in a special care unit: an exploratory investigation. Perspectives of the ASHA Special Interest Groups. 2016;1(SIG 15):68-78. doi:10.1044/persp1.SIG15.68.
  7. Mark J, Onaral B, Ayaz H. Evaluating neural correlates of constant therapy neurorehabilitation task battery: an fNIRS pilot study. Presented at: 18th International Conference on Human-Computer Interaction; July 17-22, 2016; Toronto, Ontario, Canada.
  8. Kiran S. How does severity of aphasia influence individual responsiveness to rehabilitation? Using big data to understand theories of aphasia rehabilitation. Semin Speech Lang. 2016;37(1):48-59. doi:10.1055/s-0036-1571358.
  9. Des Roches CA, Balachandran I, Ascenso E, Tripodis Y, Kiran S. Effectiveness of an impairment-based individualized rehabilitation program using an iPad-based software platform. Front Hum Neurosci. 2015;8:1015. doi:10.3389/fnhum.2014.01015.
  10. Kiran S, Des Roches CA, Balachandran I, Ascenso E. Development of an impairment-based individualized treatment workflow using an iPad-based software platform. Semin Speech Lang. 2014;35(1):38-50. doi:10.1055/s-0033-1362995.
  11. Kiran S. Detecting small and large scale fluctuations in language and cognitive performance: a longitudinal rehabilitation case study. Int J Phys Med Rehabil. 2014;2(suppl):203. doi:10.4172/2329-9096.1000203.
Posters
  1. Swathi Kiran, Kyle Gerst, Jason Godlove, Veera Anantha, Emily Dubas. Understanding optimal dosage frequency and patient engagement on improving outcomes using digital therapy. Presented at the International Stroke Conference 2019.

  2. Emily Dubas, Jason Godlove, Swathi Kiran, Kyle Gerst. Understanding enablers and barriers to using technology with people with aphasia. Presented at the Aphasia Access Leadership Summit 2019

  3. Jason Godlove, Emily Dubas, Mahendra Advani, David Poskanzer, Swathi Kiran. Using technology to understand patient reported outcomes of life participation after aphasia. Presented at the Aphasia Access Leadership Summit 2019

  4. Jason Godlove, Swathi Kiran. Understanding the effect of patient severity on aphasia rehabilitation outcomes. Presented at the American Congress of Rehabilitation Medicine (ACRM) Annual Conference 2017

  5. Jason Godlove, Veera Anantha, Swathi Kiran. Is home based therapy as effective in-clinic therapy for patients with aphasia. Presented at the American Congress of Rehabilitation Medicine (ACRM) 94th Annual Conference 2017

View the Clinical Evidence behind Constant Therapy
  1. Abel S, Willmes K, Huber W. Model‐oriented naming therapy: testing predictions of a connectionist model. Aphasiology. 2007;21(5):411-447. doi:10.1080/02687030701192687.
  2. Annoni JM, Khateb A, Custodi MC, Debeauvais V, Michel CM, Landis T. Advantage of semantic language therapy in chronic aphasia: a study of three cases. Aphasiology. 1998;12(12):1093-1105. doi:10.1080/02687039808249475.
  3. Ball AL, de Riesthal M, Breeding, VE, Mendoza DE. Modified ACT and CART in severe aphasia. Aphasiology. 2011;25(6-7):836-848. doi:10.1080/02687038.2010.544320.
  4. Baldwin VN, Powell T. Google Calendar: A single case experimental design study of a man with severe memory problems. Neuropsychol Rehabil. 2015;25(4):617-636. doi:10.1080/09602011.2014.956764.
  5. Beeson PM, Egnor H. Combining treatment for written and spoken naming. J Int Neuropsychol Soc. 2006;12(6):816-827. doi:10.1017/S1355617706061005.
  6. Beeson PM, Rising K, Kim ES, Rapcsak SZ. A novel method for examining response to spelling treatment. Aphasiology. 2008;22(7-8):707-717. doi:10.1080/02687030701800826.
  7. Beeson PM, Hirsch FM, Rewega MA. Successful single-word writing treatment: experimental analyses of four cases. Aphasiology. 2002;16(4-6):473-491. doi:10.1080/02687030244000167.
  8. Beeson PM, Rising K, Volk J. Writing treatment for severe aphasia: who benefits? J Speech Lang Hear Res. 2003;46(5):1038-1060. doi:10.1044/1092-4388(2003/083).
  9. Berryman A, Rasavage K, Politzer T. Practical clinical treatment strategies for evaluation and treatment of visual field loss and visual inattention. NeuroRehabilitation. 2010;27(3):261-268. doi:10.3233/NRE-2010-0607.
  10. Brunsdon R, Nickels L, Coltheart M, Joy P. Assessment and treatment of childhood topographical disorientation: a case study. Neuropsychol Rehabil. 2007;17(1):53-94. doi:10.1080/09602010600562575.
  11. Cicerone KD. Remediation of “working attention” in mild traumatic brain injury. Brain Inj. 2002;16(3):185-195. doi:10.1080/02699050110103959.
  12. Cipriani G, Bianchetti A, Trabucchi M. Outcomes of a computer-based cognitive rehabilitation program on Alzheimer’s disease patients compared with those on patients affected by mild cognitive impairment. Arch Gerontol Geriatr. 2006;43(3):327-335. doi:10.1016/j.archger.2005.12.003.
  13. Corsten S, Mende M, Cholewa J, Huber W. Treatment of input and output phonology in aphasia: a single case study. Aphasiology. 2007;21(6-8):587-603. doi:10.1080/02687030701192034.
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  15. Doesborgh SJ, van de Sandt-Koenderman MW, Dippel DW, van Harskamp F, Koudstaal PG, Visch-Brink EG. Effects of semantic treatment on verbal communication and linguistic processing in aphasia after stroke: a randomized controlled trial. Stroke. 2004;35(1):141-146. doi:10.1161/01.STR.0000105460.52928.A6.
  16. Domahs F, Bartha-Doering L, Delazer M. Rehabilitation of arithmetic abilities: different intervention strategies for multiplication. Brain and Language. 2003;87(1):165-166. doi:10.1016/S0093-934X(03)00252-9.
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  21. Ehlhardt LA, Sohlberg MM, Glang A, Albin R. TEACH-M: a pilot study evaluating an instructional sequence for persons with impaired memory and executive functions. Brain Inj. 2005;19(8):569-583. doi:10.1080/02699050400013550.
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  24. Franklin S, Buerk F, Howard D. Generalised improvement in speech production for a subject with reproduction conduction aphasia. Aphasiology. 2002;16(10-11):1087-1114. doi:10.1080/02687030244000491.
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  26. Girelli L, Seron X. Rehabilitation of number processing and calculation skills. Aphasiology. 2001;15(7):695-712. doi:10.1080/02687040143000131.
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  32. Kiran S, Thompson CK. The role of semantic complexity in treatment of naming deficits: training semantic categories in fluent aphasia by controlling exemplar typicality. J Speech Lang Hear Res. 2003;46(3):608-622.
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  34. Kiran S, Johnson L. Semantic complexity in treatment of naming deficits in aphasia: evidence from well-defined categories. Am J Speech Lang Pathol. 2008;17(4):389-400. doi:10.1044/1058-0360(2008/06-0085).
  35. Kiran S, Viswanathan M. Effect of model-based treatment on oral reading abilities in severe alexia: a case study. J Med Speech Lang Pathol. 2008;16(1):43-59.
  36. Kiran S, Sandberg C, Abbot K. Treatment for lexical retrieval using abstract and concrete words in persons with aphasia: effect of complexity. Aphasiology. 2009;23(7):835-853. doi:10.1080/02687030802588866.
  37. Kiran S, Sandberg C, Sebastian R. Treatment of category generation and retrieval in aphasia: effect of typicality of category items. J Speech Lang Hear Res. 2011;54(4):1101-1117. doi:10.1044/1092-4388(2010/10-0117).
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  39. Kiran S. Training phoneme to grapheme conversion for patients with written and oral production deficits: a model-based approach. Aphasiology. 2005;19(1):53-76. doi:10.1080/02687030444000633.
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