List of algorithms

[rmndrs89]

Dear all,

this topic is to serve as a checklist of algorithms that could be/have been implemented.

=============== Low Back-Worn IMU ===============

Gait Sequence Detection

• Paraschiv-Ionescu et al. (2019). Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions. Journal of NeuroEngineering and Rehabilitation, 16(1), 24. https://doi.org/10.1186/s12984-019-0494-z
• Paraschiv-Ionescu et al. (2020). Real-world speed estimation using single trunk IMU: methodological challenges for impaired gait patterns. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 4596–4599. https://doi.org/10.1109/EMBC44109.2020.9176281
• Pham et al. (2017). Validation of a Step Detection Algorithm during Straight Walking and Turning in Patients with Parkinson's Disease and Older Adults Using an Inertial Measurement Unit at the Lower Back. Frontiers in Neurology, 8, 457. https://doi.org/10.3389/fneur.2017.00457
• Hickey et al. (2017). Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis. Physiological Measurement, 38(1), N1–N15. https://doi.org/10.1088/1361-6579/38/1/N1

Initial Contact Detection

• Paraschiv-Ionescu et al. (2020). Real-world speed estimation using single trunk IMU: methodological challenges for impaired gait patterns. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 4596-4599. https://doi.org/10.1109/EMBC44109.2020.9176281
• Paraschiv-Ionescu et al. (2019). Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions. Journal of NeuroEngineering and Rehabilitation, 16(1), 24. https://doi.org/10.1186/s12984-019-0494-z

Sit-to-Stand and Stand-to-Sit Detection

• Pham et al. (2018). Validation of a Lower Back "Wearable"-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment. Frontiers in Neurology, 9, 652. https://doi.org/10.3389/fneur.2018.00652
• Hickey et al. (2016). A multi-resolution investigation for postural transition detection and quantification using a single wearable. Gait & Posture, 49, 411–417. https://doi.org/10.1016/j.gaitpost.2016.07.328
• Atrsaei et al. (2020). Postural transitions detection and characterization in healthy and patient populations using a single waist sensor. Journal of NeuroEngineering and Rehabilitation, 17(1), 70. https://doi.org/10.1186/s12984-020-00692-4
• Adamowicz et al. (2020). Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back. Sensors (Basel, Switzerland), 20(22), 6618. https://doi.org/10.3390/s20226618

Turn Detection

• Pham et al. (2017). Algorithm for Turning Detection and Analysis Validated under Home-Like Conditions in Patients with Parkinson's Disease and Older Adults using a 6 Degree-of-Freedom Inertial Measurement Unit at the Lower Back. Frontiers in Neurology, 8, 135. https://doi.org/10.3389/fneur.2017.00135
• El-Gohary et al. (2013). Continuous monitoring of turning in patients with movement disability. Sensors (Basel, Switzerland), 14(1), 356–369. https://doi.org/10.3390/s140100356
• Shah et al. (2021). Inertial Sensor Algorithms to Characterize Turning in Neurological Patients With Turn Hesitations. IEEE Transactions on Bio-medical engineering, 68(9), 2615–2625. https://doi.org/10.1109/TBME.2020.3037820

Nocturnal Movements (a.k.a. Sleep Analysis Algorithm)

• Louter et al. (2015). Accelerometer-based quantitative analysis of axial nocturnal movements differentiates patients with Parkinson's disease, but not high-risk individuals, from controls. J Neurol Neurosurg Psychiatry, 86, 32--37. https://jnnp.bmj.com/content/86/1/32
• Mirelman et al. (2020). Tossing and Turning in Bed: Nocturnal Movements in Parkinson's Disease. Movement Disorders, 35(6):959--968. https://doi.org/10.1002/mds.28006

=============== Wrist-Worn IMU ===============
Physical Activity Monitoring

• Van Hees, Vincent T., et al. (2013). Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PloS one 8.4. https://doi.org/10.1371/journal.pone.0061691

[JuliusWelzel]

@masoudabedinifar Can you indicate when you work on an algorithm from the list?

[masoudabedinifar]

@JuliusWelzel Currently I am working on updating of gsd algorithm based on the new organization order:
(Paraschiv-Ionescu et al. (2019). Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions. Journal of NeuroEngineering and Rehabilitation, 16(1), 24. https://doi.org/10.1186/s12984-019-0494-z --> Paraschiv-Ionescu et al. (2020). Real-world speed estimation using single trunk IMU: methodological challenges for impaired gait patterns. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 4596–4599. https://doi.org/10.1109/EMBC44109.2020.9176281)

[masoudabedinifar]

Dear all,

Currently, I am re-organizing icd algorithm (ParaschivIonescuInitialContactDetection) which could be referred as:

1. Paraschiv-Ionescu et al. (2020). Real-world speed estimation using single trunk IMU: methodological challenges for impaired gait patterns. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 4596-4599. https://doi.org/10.1109/EMBC44109.2020.9176281
2. Paraschiv-Ionescu et al. (2019). Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions. Journal of NeuroEngineering and Rehabilitation, 16(1), 24. https://doi.org/10.1186/s12984-019-0494-z

Best,
Masoud.

[masoudabedinifar]

Dear all,

Currently, I am re-organizing pam algorithm (Physical Activity Monitoring) which could be referred to as:

1. Doherty, Aiden, et al. (2017). Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study. PloS one 12.2. https://doi.org/10.1371/journal.pone.0169649
2. Van Hees, Vincent T., et al. (2013). Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PloS one 8.4. https://doi.org/10.1371/journal.pone.0061691

Best,
Masoud.

[masoudabedinifar]

Dear all,

Currently, I am working on the translation and organization of the Sit-to-Stand and Stand-to-Sit Detection algorithm with the branch name ss-dev:
Pham et al. (2018). Validation of a Lower Back "Wearable"-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment. Frontiers in Neurology, 9, 652. https://doi.org/10.3389/fneur.2018.00652

Best,
Masoud.

[masoudabedinifar]

Dear all,

Currently, I am working on the translation and organization of the Turn Detection algorithm with the branch name turning-detection-dev:
Pham et al. (2017). Algorithm for Turning Detection and Analysis Validated under Home-Like Conditions in Patients with Parkinson's Disease and Older Adults using a 6 Degree-of-Freedom Inertial Measurement Unit at the Lower Back. Frontiers in Neurology, 8, 135. https://doi.org/10.3389/fneur.2017.00135

Best,
Masoud.

[rmndrs89]

Hi @masoudabedinifar,

for the sleep analysis I have added two references that should help you set up the algorithms.
The second one (i.e., Mirelman et al.) describes more in detail the methods, but it refers to the other one (i.e., Louter et al.).

Best,
Robbin