Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom

By M. Bächlin, M. Plotnik, Daniel Roggen, I. Maidan, Jeffrey M. Hausdorff, Nir Giladi, Gerhard Tröster. IEEE Transactions on Information Technology in Biomedicine, 2010

675 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Neurology, Physical Therapy, Sports Therapy and Rehabilitation

View Paper: https://www.semanticscholar.org/paper/1c55427d34e3bf2137152cebd0ffbdf83ff32403

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In Biomedical Engineering

In Physical Therapy, Sports Therapy and Rehabilitation

Paolo Bonato

Citations in the corpus

A review of wearable sensors and systems with application in rehabilitation (2012, background): “Bachlin et al [84] developed a system to detect freezing of gait (FOG), a commonly found gait symptom in Parkinson ’ s disease that is highly related to falls.”
Deep Recurrent Neural Networks for Human Activity Recognition (2017, background): “4) Daphnet FOG [22]: Dataset containing movement data from patients with Parkinson’s disease (PD) who suffer from freezing of gait (FOG) symptoms.”
A Survey on Ambient Intelligence in Health Care (2013, positive): “[204] also introduce a wearable assistant for Parkinson’s disease patients with the freezing of gait (FOG) symptom.”
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, background): “Most of such sensors are embedded into smartphones and smartwatches, and the number of activities in these datasets ranges from two [15] to thirty-three [18], as shown in Table 6.”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, methodology): “6 50–200 Hz 37 Daily & Sports Activities [19] Locomotion 9D IMU 19 25 Hz 37 MHEALTH [30, 31] Locomotion & gesture 9D IMU & ECG 12 50 Hz 33 Opportunity [59] Locomotion & gesture 9D IMU 16 50 Hz 32 PAMAP2 [228] Locomotion & activities 9D IMU & HR monitor 18 100 Hz 32 Daphnet [23] Freezing of gait 3D A”
Anomaly Detection in Time Series: A Comprehensive Evaluation (2022, neutral): “1% 1 1 Daphnet [7, 41] real multi 32,594 13.”
A Survey of Deep Active Learning (2020, background): “[95] OPPORTUNITY [95], WISDM [122], SenseBox [219], Skoda Daphnet [15],CASAS [40] Smart home”
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (2017, background): “For example, freeze of gait in Parkinson’s is a common problem amongst patients but throughout the day such freezing episodes are rare – though very critical w.r.t. health and wellbeing of the patient and thus require reliable recognition (e.g., [Bachlin et al. 2010]).”
Deep Learning for Sensor-based Human Activity Recognition (2020, background): “For recognizing human activities, accelerometers can be mounted on various parts of a body, such as the waist [7], arm [173], ankle [10], wrist [61], et al.”