Library · Signals, sensing & imaging
What Wearable & Mobile Sensing got wrong, from 40 theses
Summary written by a model from the records below: The records demonstrate that wearable and mobile sensing deployments frequently struggle with sensor placement instability, physiological variability, and user non-compliance. Furthermore, proprietary algorithms, sparse kinematic configurations, and fixed robotic assistance profiles often fail to generalize across diverse operating conditions. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 7 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Sparse inertial sensing and isolated sensor placements fail to capture complex kinematics and kinetic forces
Single-point inertial measurement units and wrist-worn accelerometers lacked the spatial degrees of freedom required to distinguish posture, reconstruct finger movements, or detect dynamic balance perturbations. Kinematic estimation from sparse inertial sensors failed to match goniometer baselines, could not reliably estimate ground reaction forces, and was susceptible to motion artifacts during real-world movement tracking.
Considered and rejected
Considered and rejected: Rejected relying solely on wearable wrist sensors because they failed to capture isolated finger movements and complex full-body kinematics.
Where is the Person in Personalized Medicine? The Missing Expert in Adaptive Neurotechnology · University of Washington · PhD
Considered and rejected
Considered and rejected: Rejected using only total acceleration (accel_total) magnitude for wrist-worn sensors because it could not discriminate between sitting and standing.
Deep Neural Network Representations of Physiological Time-Series Sensor Data for Improved Recognition Performance · Texas State University · PhD
Tried and failed
adding triaxial accelerometry to multimodal sensing applied to sleep wake detection. Outcome: no signal. Reason: wrist accelerometry features did not provide additional discriminative value beyond combined video and PPG
Combining wearables and nearables for patient state analysis · Georgia Tech · PhD
Tried and failed
kinematic inertial sensor estimation applied to ground reaction force estimation. Outcome: worse than baseline. Reason: high stride-by-stride kinematic variability and non-sagittal compensatory movements degraded accuracy relative to direct force sensing
Experimental and Analytical Methods for Understanding User Gait Biomechanics in Response to Soft Ankle Exosuits · Harvard · PhD
Tried and failed
inertial motion capture for joint angle measurement applied to human joint kinematics validation. Outcome: worse than baseline. Reason: inertial sensors failed to agree with baseline goniometer measurements across multiple joints
Integration of Physical and Psychological Human Data into Digital Human Modelling for Manufacturing System Design · Cranfield · PhD
Tried and failed
single-point inertial measurement unit tracking applied to complex exercise motion classification. Outcome: no signal. Reason: single-channel kinematics lacked sufficient spatial degrees of freedom without multi-site bodily strain sensing
Study Of Intelligent Wearables and Adaptive Systems to Measure Physiological and Physical Data for Human-Machine Interfaces · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected calculating stepping kinematics and trunk angular velocity from wearable IMUs in the real world due to sensitivity to motion artifacts and inter-sensor distance estimation errors.
Non-Treadmill Trip Training – Laboratory Efficacy, Validation of Inertial Measurement Units, and Tripping Kinematics in the Real World · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Rejected measuring physical activity with simple accelerometers alone in population health studies, recommending wearable devices that incorporate continuous heart-rate monitoring.
Essays on Selection in Health Behaviors · Harvard · PhD
Wearability burden, frequent charging, and adherence difficulties undermine continuous tracking
Multi-device configurations across bilateral limbs and continuous wearable tracking were rejected or failed due to high wearing discomfort, charging demands, and low compliance in cognitively impaired cohorts. Participants frequently deactivated passive mobile sensing streams to conserve battery, while bodily worn sensors were deemed impractical for natural walk-up public scenarios and gesture recognition.
Considered and rejected
Considered and rejected: Rejected bodily worn tracking sensors due to lack of suitability for natural walk-up-and-use public scenarios.
Considered and rejected
Considered and rejected: Rejected multi-device wearable setups (bilateral feet/wrists/sternum) for continuous free-living monitoring due to high wearability burden, reduced patient compliance, and battery charging demands.
Development and evaluation of computational methods for measuring free-living gait and uncovering neuropathology in Parkinson’s disease · Boston University · PhD
Considered and rejected
Considered and rejected: Wearable sensors for hand gesture recognition were rejected because they are expensive, complex, and unnatural.
Human-Interactions with Robotic Cyber-Physical Systems (CPS) for Facilitating Construction Progress Monitoring · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Excluded wearable sleep trackers in favor of under-the-mattress sensors to avoid data loss from short battery life, frequent charging, and adherence issues in cognitively impaired populations.
From behavioural patterns to rain barriers: representation learning for ageing and dementia · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected wearable multi-device setups (e.g., S-Smart pocket smartphone + wrist IMU + foot IMU) due to daily living inconvenience
Smartwatch-based semantic learning of elements of human behaviour · Oxford · PhD
Considered and rejected
Considered and rejected: Rejected using smartphone passive sensing streams (e.g., Bluetooth device counts) due to participant deactivations for battery conservation causing excessive missingness.
Forecasting depressive symptom deterioration using wearable sensor data and LSTM models: a longitudinal analysis from the RADAR-MDD Study · Nottingham · Doctorate
Tried and failed
wearable activity tracking applied to cognitively impaired older adults. Reason: poor user adoption, adherence, and tolerance leading to non-compliance
Monitoring the effects of traumatic brain injury in older adults using a remote home monitoring system · Imperial · Doctorate
Considered and rejected
Considered and rejected: Rejected wearable devices for continuous tracking due to low patient compliance associated with cognitive impairments
Monitoring behavioural changes in households with dementia using intelligent ambient sensing technologies · Imperial · PhD
Physiological and mobile signals suffer from environmental confounders and poor discriminative power
Ambient temperature caused galvanic skin response runaway and prior exercise disrupted heart rate variability ratios, while peripheral temperature distributions showed wide overlap across affective states. Sensor features failed to correlate with breathing-zone pollutant exposure, could not detect seizures lacking tachycardia, and lacked predictive signal for clinical depression scale scores.
Tried and failed
multimodal physiological stress detection with wearable sensors applied to autonomic tone and stress monitoring. Outcome: unstable. Reason: High ambient temperature caused GSR runaway and prior exercise disrupted HRV LF/HF ratio reliability
A Skin-Like Sternal Patch to Monitor Autonomic Tone During Cognitive Stress and Sympathetic Arousals in Disordered Sleep · Georgia Tech · PhD
Tried and failed
wearable physiological sensors for affective state detection applied to detecting emotional responses to transient events. Outcome: no signal. Reason: sensors failed to reliably isolate heart rate changes triggered by transient unexpected errors
Artificial social constructivism for long term human computer interaction · Imperial · PhD
Tried and failed
heuristic feature normalization without patient baseline applied to physiological regression from wearable sensor features. Outcome: no signal. Reason: external statistics and weight scaling failed to capture individual physiological baseline variability
Towards real-time estimation of decompensation status in acute pathological conditions leveraging wearable technology and machine learning · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Attempting detection of FIA seizures lacking tachycardia, rejected as not sufficiently stereotyped across patients for consumer wearable sensors.
Focal Impaired Awareness Seizure Detection Using a Smartwatch · Johns Hopkins · Master's
Considered and rejected
Considered and rejected: Rejected electromyography (EMG) sensors for obtaining muscle forces due to signal fuzziness and sensitivity to environmental conditions
Efficient and Safe Robot Planning in Human-Robot Collaboration · University at Buffalo · PhD
Tried and failed
gradient boosted trees with automated feature selection applied to depression prediction from smartphone sensing. Outcome: no signal. Reason: passive mobile sensor features lacked detectable predictive signal for depression scale scores
Applying lessons from a critical analysis of machine learning in psychological science : two examples predicting psychological traits and states using voice data · UT Austin · PhD
Tried and failed
predicting environmental exposure from wearable physiological signals applied to personal inhalation exposure estimation. Outcome: no signal. Reason: physiological metrics alone lack sufficient correlation with breathing-zone pollutant concentrations across varied activities
Assessment of Monitoring Strategies for Inhalation Exposure and Occupancy in Office Environments · EPFL · PhD
Sensor displacement and mechanical coupling shifts degrade wearable signal quality
Wearable interface condition changes caused spatial shifts in activation patterns, and fixed proximity thresholds yielded high false negative rates when identifying sensor detachment. Direct skin taping made consistent cross-session sensor placement difficult without trained professionals, tensile transducer stretching caused pressure cancellation, and wrist locations underperformed chest placements.
Tried and failed
single sensor placement optimization for signal quality applied to biosignal decoding across mechanical conditions. Outcome: did not generalise. Reason: Spatial shifts in signal activation patterns occurred when physical loading or wearable interface conditions changed.
Learning steps: models of intent-driven lower limb prosthesis use · Imperial · PhD
Tried and failed
threshold-based proximity sensing for contact detection applied to wearable sensor coupling validation. Outcome: did not generalise. Reason: High false negative rate yielding only 29.7% sensitivity in detecting sensor detachment events.
Optimal Head Impact Signal Processing and the Description and Perception of Head Impact Exposure in Female Adolescent Ice Hockey Players · Virginia Tech · Doctorate
Tried and failed
tensile strain mode physiological transducer placement applied to respiratory monitoring wearable sensor. Outcome: worse than baseline. Reason: stretching mode caused pressure cancellation, resulting in substantially lower sensitivity than compression mode
Stretchable wearable wireless sensors for physiological monitoring of humans and dogs · Imperial · PhD
Lost to a baseline
Wrist-worn sensors underperformed chest-worn sensors across all 5 holding assessment scenarios (0.738 vs 0.870 accuracy in Scenario 1)
Leveraging pervasive data to study and support mother-infant dyads in the wild · UT Austin · PhD
Considered and rejected
Considered and rejected: Rejected attaching strain sensors directly to the skin with tape due to difficulty in consistently replicating sensor placement across sessions without a trained professional.
Stretch sensors for measuring knee kinematics in sports · Imperial · PhD
Fixed thresholds and proprietary vendor algorithms fail to generalize or accurately detect events
Proprietary vendor event classification algorithms misclassified genuine impacts as spurious artifacts and showed low accuracy compared to validated activity diaries. Fixed state machine thresholds triggered frequent false positive activations across varying postures, and fixed percentage outlier thresholds generalized poorly across individual sensors.
Tried and failed
threshold-based electromyography state machine controller applied to wearable movement assistance. Outcome: did not generalise. Reason: fixed thresholding caused frequent false positive activations across varying task postures and movement heights
Assisting and Evaluating Upper Extremity Movements with Wearables Systems · Harvard · PhD
Tried and failed
proprietary vendor event classification algorithm applied to wearable kinematic sensor impact detection. Outcome: did not generalise. Reason: algorithm misclassified genuine impacts as spurious artifacts and missed true events compared to video verification
Considered and rejected
Considered and rejected: Rejected using wearable sensor spectral monitoring data and activity diaries for quantitative prior-light-history tracking due to sensor inaccuracies/malfunctioning and inconsistent participant reporting.
Alertness in work environments : on the role of indoor daylight exposure · EPFL · PhD
Considered and rejected
Considered and rejected: Using a fixed percentage threshold for Mahalanobis distance outlier removal was rejected because it generalizes poorly across individual sensors compared to probability-based thresholds.
Security and Reliability in Pervasive Computing · Politecnico di Torino · PhD
Tried and failed
wearable sensor heuristic activity classification applied to travel mode and physical activity detection. Outcome: worse than baseline. Reason: built-in proprietary algorithms showed low accuracy compared to validated self-reported travel diaries
Applications of causal inference in environmental policy and transport studies · Imperial · Doctorate
Discrepancies across hardware devices and populations impair cross-subject model transferability
Integrating heterogeneous consumer devices suffered from inter-device measurement variability, and research-grade wearables exhibited higher resting heart rate measurement error than consumer units. Adding extra inertial measurement units degraded cross-subject generalization for speed estimation, while multi-country generalized models were outperformed by localized region-specific models.
Tried and failed
integrating heterogeneous consumer wearable sensor streams applied to remote physical activity tracking. Reason: inter-device measurement variability undermined data comparability across study participants
Rehabilitation through exercise prescription for cardiac patients using an artificial intelligence-based programme · Imperial · PhD
Tried and failed
training subpopulation-specific machine learning models applied to movement classification from wearable sensors. Outcome: worse than baseline. Reason: smaller subpopulation training dataset size hurt generalization compared to pooling all subject data
Fall detection and balance control during human movement · UT Austin · PhD
Lost to a baseline
Research-grade wearables (Empatica E4, Biovotion Everion) had higher resting HR measurement error (MAE 13.9 ± 7.8 bpm) than consumer devices (MAE 7.2 ± 5.4 bpm).
Discovering Digital Biomarkers of Glycemic Health from Wearable Sensors · Duke · Doctorate
Lost to a baseline
Multi-country generalized machine learning models were outperformed by continent-specific and country-specific models for smartphone-sensing-based mood and activity inference.
The iLog methodology for fostering valid and reliable Big Thick Data · University of Trento · PhD
Tried and failed
multi-sensor fusion with additional inertial measurement units applied to locomotion speed estimation. Outcome: worse than baseline. Reason: additional sensors did not reduce error and degraded user-independent cross-subject generalization
Sensor Fusion Representation of Locomotion Biomechanics with Applications in the Control of Lower Limb Prostheses · Georgia Tech · PhD
Fixed biomimetic torque profiles fail to adapt to individual dynamics in wearable robotic assistance
Applying biologically inspired fixed torque profiles failed to produce statistically significant metabolic reductions compared to unassisted walking. Mismatched actuation timing and force profiles disrupted natural user biomechanics and increased metabolic effort relative to individualized human-in-the-loop optimization.
Tried and failed
biomimetic torque profiles for wearable assistance applied to locomotion metabolic cost reduction. Outcome: no signal. Reason: fixed biological torque profiles failed to yield statistically significant metabolic reductions compared to unassisted walking
Soft Exosuits for Improved Walking Efficiency and Community Based Post-Stroke Gait Rehabilitation · Harvard · PhD
Tried and failed
applying mismatched actuation profiles across movement modes applied to assistive wearable robotics control. Outcome: worse than baseline. Reason: incorrect timing and force profile disrupted natural biomechanics and increased metabolic effort
Soft Hip Exosuit Optimization for Improving Mobility in Healthy Young Adults and Individuals with Parkinson’s Disease · Harvard · PhD
Tried and failed
biomimetic control timing parameterization applied to wearable robotic gait assistance. Outcome: worse than baseline. Reason: biologically-inspired profiles failed to account for individual user dynamics compared to individualized human-in-the-loop optimization
Soft Hip Exosuit Optimization for Improving Mobility in Healthy Young Adults and Individuals with Parkinson’s Disease · Harvard · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Test the smart wearable system on a larger, diverse population beyond healthy young adults for dynamic cardiac and respiration monitoring. Blocker: Requires custom physical wearable hardware and recruitment of human clinical cohorts for dynamic physiological data collection
A Novel Smart Wearable System with Edge Computing AI for Cardiac Disease Detection and Continuous Monitoring · Texas Tech · PhD
Left open
Develop calibration-free generalized torque models or bodyweight-based calibration procedures for multimodal wearable sensors without isokinetic dynamometers. Blocker: Requires custom multimodal hardware (IMUs and soft sensors), human subject testing, and ground-truth biomechanical measurement equipment
Tracking Joint Kinematics and Muscle Kinetics with Multimodal Wearable Sensors · Harvard · PhD
Left open
Develop wearable multimodal sensing methods to detect postprandial hypotension and nocturnal non-dipping blood pressure. Blocker: Requires specialized wearable multimodal cardiopulmonary sensor hardware and clinical study data on CVAD patients.
Advancing Wearable Cardiopulmonary Monitoring Through Multimodal Sensing of Cardiovascular, Pulmonary, and Respiratory Muscle Function · Georgia Tech · PhD
Left open
Develop adaptive signal selection algorithms for redundant sensor arrays to dynamically correct for wearable placement misalignment without ground truth data. Blocker: Requires custom physical wearable sensor hardware (SCARS soft strain gauges, ultrasound arrays) and real-time physical acquisition apparatus
Multi-Modal Deformation Sensing for Evaluating Muscle Function · Harvard · PhD
Left open
Integrate digital accelerometers and input force sensors into wearable active vibration hardware while reducing the accelerometer count to a single output. Blocker: Requires designing, fabricating, and testing custom physical wearable sensor hardware
Novel Methods Using Acoustics and Bioimpedance to Assess Musculoskeletal Health and Performance · Georgia Tech · PhD
Left open
Evaluate joint feature estimation, spatio-temporal sEMG heatmaps, and alternative neural network architectures for multi-step muscle fatigue forecasting horizons. Blocker: Requires sEMG data from the custom wearable device developed in the thesis
Lower back muscle activity and fatigue during activities of daily living using a novel wearable device and machine learning · Imperial · PhD
Left open
Implement the dynamic EMG decomposition and control pipeline on embedded wearable hardware and evaluate hardware trade-offs. Blocker: Requires custom embedded hardware design and wearable physical apparatus/sensors for physical testing.
Non-invasive neural interfacing for wearable electromyographic systems · Imperial · PhD
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Jinsoo Kim · Harvard · PhD · 2022
Soft Hip Exosuit Optimization for Improving Mobility in Healthy Young Adults and Individuals with Parkinson’s Disease - Eric Wert · Harvard · PhD · 2026
Essays on Selection in Health Behaviors - Nan Fletcher-Lloyd · Imperial · PhD · 2025
From behavioural patterns to rain barriers: representation learning for ageing and dementia - Pasan Witharana · Imperial · PhD · 2025
Rehabilitation through exercise prescription for cardiac patients using an artificial intelligence-based programme - Yoon Jae Lee · Georgia Tech · PhD · 2025
Study Of Intelligent Wearables and Adaptive Systems to Measure Physiological and Physical Data for Human-Machine Interfaces - John Berkebile · Georgia Tech · PhD · 2025
Advancing Wearable Cardiopulmonary Monitoring Through Multimodal Sensing of Cardiovascular, Pulmonary, and Respiratory Muscle Function - Yu Meng Zhou · Harvard · PhD · 2025
Assisting and Evaluating Upper Extremity Movements with Wearables Systems - Yichu Jin · Harvard · PhD · 2025
Tracking Joint Kinematics and Muscle Kinetics with Multimodal Wearable Sensors - Megan Parkinson · Imperial · Doctorate · 2025
Monitoring the effects of traumatic brain injury in older adults using a remote home monitoring system - Fintan James Haley · Nottingham · Doctorate · 2025
Forecasting depressive symptom deterioration using wearable sensor data and LSTM models: a longitudinal analysis from the RADAR-MDD Study - Mahfuzur Rahman · Texas Tech · PhD · 2025
A Novel Smart Wearable System with Edge Computing AI for Cardiac Disease Detection and Continuous Monitoring - Bálint Hodossy · Imperial · PhD · 2024
Learning steps: models of intent-driven lower limb prosthesis use
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