Publications

Awais et al. (2021). Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification

Sensors

Gould et al. (2021). Computational modelling of the scoliotic spine: A literature review

Numerical Methods in Biomedical Engineering

Kamiar et al. (2021). Real-world speed estimation using single IMU: A conceptual framework

3D-AHM Abstract Book

Angelini et al. (2021). A Multifactorial Model of Multiple Sclerosis Gait and its Changes Across Different Disability Levels

IEEE Transactions on Biomedical Engineering

Del Din et al. (2021). Body-Worn Sensors for Remote Monitoring of Parkinson’s Disease Motor Symptoms: Vision, State of the Art, and Challenges Ahead

Journal of Parkinson’s Disease

Ibrahim et al. (2020). Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis

Journal of NeuroEngineering and Rehabilitation

Soltani (2020). Gait in real world: validated algorithms for gait periods and speed estimation using a single wearable sensor.

EPFL

Bonci et al. (2020). An objective methodology for the selection of a device for continuous mobility assessment

Sensors

Galperin et al. (2020). Sensor-Based and Patient-Based Assessment of Daily-Living Physical Activity in People with Parkinson’s Disease: Do Motor Subtypes Play a Role?

Sensors

Rochester et al. (2020). A roadmap to inform development, validation and approval of digital mobility outcomes: the Mobilise-D approach

Digital Biomarkers

Viceconti et al. (2020). Toward a Regulatory Qualification of Real-World Mobility Performance Biomarkers in Parkinson’s patients Using Digital Mobility Outcomes

Sensors

Caruso et al. (2020). Orientation Estimation Through Magneto-Inertial Sensor Fusion: A Heuristic Approach for Suboptimal Parameters Tuning

IEEE Sensors Journal

Gaßner et al. (2020). Clinical Relevance of Standardized Mobile Gait Tests. Reliability Analysis Between Gait Recordings at Hospital and Home in Parkinson’s Disease: A Pilot Study

Journal of Parkinson’s Disease

Paraschiv-Ionescu et al. (2020). Real-world speed estimation using single trunk IMU: methodological challenges for impaired gait patterns

IEEE

Polhemus et al. (2020). Walking-related digital mobility outcomes as clinical trial endpoint measures: protocol for a scoping review

BMJ Open

Angelini et al. (2020). Wearable sensors can reliably quantify gait alterations associated with disability in people with progressive multiple sclerosis in a clinical setting

Journal of Neurology

Shema-Shiratzky et al. (2020). A wearable sensor identifies alterations in community ambulation in multiple sclerosis: contributors to real-world gait quality and physical activity

Journal of Neurology

Ullrich et al. (2020). Detection of gait from continuous inertial sensor data using harmonic frequencies

IEEE Journal of Biomedical and Health Informatics

Gaßner et al. (2020). Gait variability as digital biomarker of disease severity in Huntington’s disease

Journal of Neurology

Warmerdam et al. (2020). Long-term unsupervised mobility assessment in movement disorders

The Lancet Neurology

Rehman et al. (2020). Accelerometry-based digital gait characteristics for classification of Parkinson’s disease: what counts?

IEEE Open Journal of Engineering in Medicine and Biology

Angelini et al. (2019). Is a wearable sensor-based characterisation of gait robust enough to overcome differences between measurement protocols? A multi-centric pragmatic study in patients with Multiple Sclerosis

Sensors

Viceconti et al. (2019). Credibility of in silico trial technologies—A theoretical framing

IEEE Journal of Biomedical and Health Informatics

Caruso et al. (2019). Accuracy of the orientation estimate obtained using four sensor fusion filters applied to recordings of magneto-inertial sensors moving at three rotation rates

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Gelder et al. (2021). A Proposal for a Linear Calculation of Gait Asymmetry

Kluge et al. (2021). Consensus based framework for digital mobility monitoring

Awais et al. (2021). Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification

Gould et al. (2021). Computational modelling of the scoliotic spine: A literature review

Kamiar et al. (2021). Real-world speed estimation using single IMU: A conceptual framework

Angelini et al. (2021). A Multifactorial Model of Multiple Sclerosis Gait and its Changes Across Different Disability Levels

Del Din et al. (2021). Body-Worn Sensors for Remote Monitoring of Parkinson’s Disease Motor Symptoms: Vision, State of the Art, and Challenges Ahead

Ibrahim et al. (2020). Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis

Soltani (2020). Gait in real world: validated algorithms for gait periods and speed estimation using a single wearable sensor.

Bonci et al. (2020). An objective methodology for the selection of a device for continuous mobility assessment

Galperin et al. (2020). Sensor-Based and Patient-Based Assessment of Daily-Living Physical Activity in People with Parkinson’s Disease: Do Motor Subtypes Play a Role?

Rochester et al. (2020). A roadmap to inform development, validation and approval of digital mobility outcomes: the Mobilise-D approach

Viceconti et al. (2020). Toward a Regulatory Qualification of Real-World Mobility Performance Biomarkers in Parkinson’s patients Using Digital Mobility Outcomes

Caruso et al. (2020). Orientation Estimation Through Magneto-Inertial Sensor Fusion: A Heuristic Approach for Suboptimal Parameters Tuning

Gaßner et al. (2020). Clinical Relevance of Standardized Mobile Gait Tests. Reliability Analysis Between Gait Recordings at Hospital and Home in Parkinson’s Disease: A Pilot Study

Paraschiv-Ionescu et al. (2020). Real-world speed estimation using single trunk IMU: methodological challenges for impaired gait patterns

Polhemus et al. (2020). Walking-related digital mobility outcomes as clinical trial endpoint measures: protocol for a scoping review

Angelini et al. (2020). Wearable sensors can reliably quantify gait alterations associated with disability in people with progressive multiple sclerosis in a clinical setting

Shema-Shiratzky et al. (2020). A wearable sensor identifies alterations in community ambulation in multiple sclerosis: contributors to real-world gait quality and physical activity

Ullrich et al. (2020). Detection of gait from continuous inertial sensor data using harmonic frequencies

Gaßner et al. (2020). Gait variability as digital biomarker of disease severity in Huntington’s disease

Warmerdam et al. (2020). Long-term unsupervised mobility assessment in movement disorders

Rehman et al. (2020). Accelerometry-based digital gait characteristics for classification of Parkinson’s disease: what counts?

Angelini et al. (2019). Is a wearable sensor-based characterisation of gait robust enough to overcome differences between measurement protocols? A multi-centric pragmatic study in patients with Multiple Sclerosis

Viceconti et al. (2019). Credibility of in silico trial technologies—A theoretical framing

Caruso et al. (2019). Accuracy of the orientation estimate obtained using four sensor fusion filters applied to recordings of magneto-inertial sensors moving at three rotation rates