Innovative Sensor-Based Approaches for Assessing Neurodegenerative Diseases: A Brief State-of-the-Art Review
Abstract
1. Introduction
Approach/Component | Description/Role |
---|---|
Holistic Systems Biology | Integrates multi-layered biological data (e.g., genomics, metabolomics) to model the interplay between host and microbiome in neurodegeneration. |
Microbiota–Gut–Brain Axis Pathways | Examines direct and indirect mechanisms (metabolites, immune modulators, barrier effects) through which the microbiome influences the CNS. |
Microbial Dysbiosis Evidence | Surveys shift in gut microbial community—particularly in Alzheimer’s and Parkinson’s disease—and explores how these imbalances contribute to NDDs. |
Dietary Modulation Strategies | Explores diet-based interventions to stimulate neuroprotective microbial metabolite production by modifying microbial composition. |
Genome-Scale Metabolic Models (GEMs) | Uses GEMs to simulate microbe–microbe and host–microbe metabolic interactions, determining the microbiome’s impact on disease development or prevention. |
Multi-Omics + GEMs for Personalized Diets | Proposes an integrative systems framework combining GEMs and omics data to design personalized, anti-inflammatory diets targeting gut microbiota in NDD prevention. |
2. Advances in Technology for NND Monitoring
2.1. Emerging Technological Innovations
2.2. Wearable and Sensor-Based Monitoring Technologies
2.3. Emerging Sensor Technologies
2.4. Digital Biomarkers
2.5. Artificial Intelligence and Machine Learning in NND Monitoring
2.6. Remote Patient Monitoring and Telemedicine
2.7. Vision-Based Approaches as Sensor-Based Technology
2.8. Clinical Implementation Progress
2.9. Real-World Applications
2.10. Integration Challenges
3. Challenges and Future Directions
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
Abbreviations | Meaning |
NDDS | Neurodegenerative diseases |
AD | Alzheimer’s disease |
PD | Parkinson’s disease |
HD | Huntington’s disease |
ALS | Amyotrophic Lateral Sclerosis |
MS | Multiple Sclerosis |
FTD | Frontotemporal Dementia |
AI | Artificial intelligence |
IoT | The internet of Things |
DeFi | Decentralized finance |
AR | Augmented reality |
VR | Virtual reality |
ML | Machine learning |
GPS | Global positioning system |
ECG | Electrocardiogram |
CGMs | Glucose monitors |
HIPAA | Health Insurance Portability and Accountability Act |
AUC | Area under the curve |
EEG | Electroencephalogram |
IMU | Inertial Measurement Unit |
RGB | Red, green, and blue |
EMG | Electromyography |
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Technology Platform | Detection Accuracy | Early Detection Capability | Clinical Validation Status |
---|---|---|---|
Wearable inertial sensors | Approximately 95% accuracy for early Parkinson’s disease detection; over 90% accuracy for Parkinson’s disease symptom detection in free-living environments. | Demonstrated capability for detecting early-stage Parkinson’s disease. | Validated in multiple studies, including real-world settings. |
Smartphone-based sensors | Approximately 98% accuracy in step length estimation, 94% accuracy in identifying gait changes for Parkinson’s disease. | Showed potential for early Parkinson’s disease detection through gait analysis. | Limited clinical validation, mostly proof-of-concept studies. |
Breath analysis | Up to 90% accuracy for Multiple Sclerosis detection; 85% accuracy for Alzheimer’s disease vs. healthy, up to 78% for Parkinson’s disease vs. healthy. | Demonstrated potential for early-stage detection. | Limited clinical validation, mostly experimental studies. |
Blood-based biomarkers (ultrasensitive detection) | High sensitivity and specificity were reported, but specific metrics were not provided. | Showed potential for early Alzheimer’s disease detection. | Promising results, but limited large-scale clinical validation. |
Eye-tracking | Receiver operating characteristic area under the curve of 0.88 for differentiating Parkinson’s disease patients from controls. | Demonstrated potential for early cognitive decline detection. | Limited clinical validation, mostly experimental studies. |
Smart home sensors | Although specific accuracy metrics were not reported, the system showed potential for monitoring long-term mild cognitive impairment. | Demonstrated capability for detecting early signs of cognitive decline. | Limited clinical validation, mostly proof-of-concept studies. |
Multi-modal systems | Up to 80% accuracy reported for combined sensor approaches. | Showed potential for comprehensive early detection. | Limited clinical validation, mostly experimental studies. |
Disease and Setting | Challenges | Limitations | Future Directions | Research Opportunities |
---|---|---|---|---|
Alzheimer’s (Clinic) | Subtle cognitive/motor decline is challenging to capture due to variability in testing environments. | Cognitive tests are often episodic, rather than continuous; imaging is also costly. | Multi-modal in-clinic sensors (eye-tracking, digital pen, EEG). | Early detection of mild cognitive impairment via digital biomarkers. |
Alzheimer’s (Home) | Adherence to wearables and noise from daily routines. | Smart-home monitoring is costly and raises privacy concerns. | Passive monitoring (speech, mobility, sleep) using IoT. | Digital phenotyping of early memory and language decline. |
Parkinson’s (Clinic) | Tremor/rigidity fluctuates; stress and meds alter readings. | Single-time-point measurements miss variability. | Digital gait labs and wearable sensors are available in the clinic. | Sensor-validated motor scoring aligned with MDS-UPDRS. |
Parkinson’s (Home) | Continuous tremor and gait monitoring → large, noisy datasets. | Device heterogeneity, patient compliance. | Smartphone-based tapping/voice apps; continuous gait sensors. | Prodromal PD detection: real-world treatment–response biomarkers. |
ALS (Clinic) | Rapid progression, heterogeneity of symptoms. | Clinical measures (ALSFRS-R) are subjective and infrequent. | Sensor-based speech and respiratory testing. | Digital endpoints for respiratory decline detection. |
ALS (Home) | Difficulty in sustained sensor use as the function declines. | Limited accessibility/adapted devices. | Voice analysis apps and respiratory wearables for home use. | Longitudinal tracking of speech/motor decline for trials. |
Huntington’s (Clinic) | Movements are variable; psychiatric symptoms are less quantifiable. | Imaging/clinical tests are limited in frequency. | Motion-capture systems; digital cognitive tests. | Quantifying subtle motor/cognitive onset before diagnosis. |
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Mbue, N.D.; Tabei, F.; Williams, K.; Olanrewaju, K. Innovative Sensor-Based Approaches for Assessing Neurodegenerative Diseases: A Brief State-of-the-Art Review. Sensors 2025, 25, 5476. https://doi.org/10.3390/s25175476
Mbue ND, Tabei F, Williams K, Olanrewaju K. Innovative Sensor-Based Approaches for Assessing Neurodegenerative Diseases: A Brief State-of-the-Art Review. Sensors. 2025; 25(17):5476. https://doi.org/10.3390/s25175476
Chicago/Turabian StyleMbue, Ngozi D., Fatemeh Tabei, Karen Williams, and Kazeem Olanrewaju. 2025. "Innovative Sensor-Based Approaches for Assessing Neurodegenerative Diseases: A Brief State-of-the-Art Review" Sensors 25, no. 17: 5476. https://doi.org/10.3390/s25175476
APA StyleMbue, N. D., Tabei, F., Williams, K., & Olanrewaju, K. (2025). Innovative Sensor-Based Approaches for Assessing Neurodegenerative Diseases: A Brief State-of-the-Art Review. Sensors, 25(17), 5476. https://doi.org/10.3390/s25175476