Evidence of Monica’s Impact
MiiCare’s work is backed by peer-reviewed clinical literature . At the core of our approach is a rigorous research driven methodology to capture and quantify the impact of our services on the health outcomes for older adults across physical, physiological and psychosocial determinants.
Peer reviewed scientific publications
Summoogum. K, Member IEEE, MiiCare Ltd, Das. D, MiiCare Ltd, Dasgupta. S, MiiCare Ltd, McLoughlin. I, Senior Member IEEE, Efstratiou. C, Member IEEE, Palaniappan. R, Senior Member IEEE. (2021). Acoustic Based Footstep Detection in Pervasive Healthcare. Publication Accepted at IEEE EMBC 2021. DOI: https://pubmed.ncbi.nlm.nih.gov/34892324/
Summoogum. Kelvin, Senior Member, IEEE, MiiHealth, UK, Das. Debayan, MiiCare, UK, Jayakumar. Parvati, MiiCare, UK. (2022). A Gait Triaging Toolkit for Overlapping Acoustic Events in Indoor Environments. Publication accepted at EMBC 2024 (Florida). DOI pending. Harvard Preprint DOI: https://ui.adsabs.harvard.edu/abs/2022arXiv221105944S/abstract
Summoogum, K., Das, D., W. John. (2023). Passive Tracking of Gait Biomarkers in Older Adults: Feasibility of an Acoustics Based Approach for Non-Intrusive Gait Analysis. Publication accepted at IEEE BSN 2023. DOI: https://ieeexplore.ieee.org/document/10331114
Summoogum, K., Das, D., & Jayakumar, P. (2023). MiiGait: A Pervasive Acoustic Based Gait Detector for At-Risk Community Dwelling Older Adult with Neurodegenerative Conditions.
White papers
Sodexo Onward Care: A Longitudinal Study of AI-powered Patient Monitoring to Reduce Avoidable Readmissions in Frail Older Adults: We discuss how Sodexo Health & Care & MiiCare created and validated a new 12-week post-discharge care pathway to reduce unnecessary bed stay by 77% with a 35% ROI on bed day cost at Stoke Manderville Hospital over a 18-month deployment.
Can Conversational AI Assistants like Monica combined with Traditional Remote Patient Monitoring Improve Transitional Care Pathways?: We present a new care model combining Monica and our remote health monitoring system to support older adults in transitional care pathways like Discharge to Assess (D2A), and address delayed discharges, high readmission rates and complexities arising during allocation of care packages.
Monica x DDM: Engaging Older People with a Virtual Assistants to Support Behavioural Change and Long Term Health Improvements - A Real World Evaluation: We present our findings from a 16-week monitoring period of delivering personalised soft interventions (provided by DDM Health) via Monica to older adults living alone at home.
Using AI based Technology to Enhance Discharge to Assess Services by Catapult Connected Places: This white paper summarises research findings from the IUK Smart Grant project “Discharge to Assess” undertaken by a consortium led by MiiCare UK, together with Connected Places Catapult, De Montfort University, Willows Health and Leicester, Leicestershire and Rutland Integrated Care Board. This project lasted from 2022 to 2024.
How MiiCube Analyses Activities of Daily Living for Remote Health Monitoring: We discuss what makes the MiiCube effective in generating personalised healthcare insights. We include 2 short case studies which demonstrate how carers, clinicians and even family members can obtain a holistic understanding of their loved ones’ wellbeing with MiiCube.
Comparing Traditional Machine Learning with Deep Learning based approaches for Applied Acoustic Gait Analysis: We provide experimental evidence to establish a precedent for using Machine Hearing for privacy-focused clinical Applications.
How MiiCube enables Effective Clinical Intervention: We briefly discuss two case studies wherein the system was able to detect behaviour anomalies. Specifically, we look at detection of a fall incident and Urinary Incontinence in two MiiCube users.
Validating the MiiCube System: The MiiCube solution uses an ecosystem of smart sensors leveraging ZIGBEE and BLE protocols to collect data on the older adult’s health and wellbeing. In this study, we document the accuracy of the sensor ecosystem to collect data of Activities of Daily Living (ADL) of older adults living in domiciliary care. The system was able to capture data for 120 ADL events across different users and home settings with an accuracy of 89%.
Capabilities of the MiiCube: We discuss 10 common pain points of older adults and their carers and demonstrate how MiiCare’s solution uses a combination of smart sensors, virtual companionship (Monica) and predictive Health AI (MiiCortex) provide value by addressing these.
MiiCare and Westminster City Council for Remote Patient Monitoring: We briefly summarise 15 use cases where MiiCare’s solution demonstrated value to WCC, ranging from tracking ADL, high fall risk scenarios and even flag potential Respiratory Tract Infections (RTIs) like COPD and COVID19.
Predicting User Behaviour - How MiiCube analyses ADL for Behaviour Anomalies: This white paper is an applied mathematical treatise in using Bayesian Learning to predict ADL and enable proactive monitoring for deviations from “expected” behaviour. We condition a Multinomial Dirichlet Model to predict ADL at various time intervals into the future. The system has the highest accuracy in predicting user behaviour in 10-minute windows into the future (at 85%) compared to 30 min and beyond (drops below 78%).