Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments

Freezing of gait (FoG) is one of the most disturbing and incapacitating symptoms in Parkinson's disease. It is defined as a sudden block in effective stepping, provoking anxiety, stress and falls. FoG is usually evaluated by means of different questionnaires; however, this method has shown to be not reliable, since it is subjective due to its dependence on patients’ and caregivers’ judgment. Several authors have analyzed the usage of MEMS inertial systems to detect FoG with the aim of objectively evaluating it. So far, specific methods based on accelerometer's frequency response has been employed in many works; nonetheless, since they have been developed and tested in laboratory conditions, their performance is commonly poor when being used at patients’ home. Therefore, this work proposes a new set of features that aims to detect FoG in real environments by using accelerometers. This set of features is compared with three previously reported approaches to detect FoG. The different feature sets are trained by means of several machine learning classifiers; furthermore, different window sizes are also evaluated. In addition, a greedy subset selection process is performed to reduce the computational load of the method and to enable a real-time implementation. Results show that the proposed method detects FoG at patients’ home with 91.7% and 87.4% of sensitivity and specificity, respectively, enhancing the results of former methods between a 5% and 11% and providing a more balanced rate of true positives and true negatives.Peer ReviewedPostprint (published version

Deep learning for detecting freezing of gait episodes in Parkinson’s disease based on accelerometers

The final publication is available at Springer via https://doi.org/10.1007/978-3-319-59147-6_30Freezing of gait (FOG) is one of the most incapacitating symptoms among the motor alterations of Parkinson’s disease (PD). Manifesting FOG episodes reduce patients’ quality of life and their autonomy to perform daily living activities, while it may provoke falls. Accurate ambulatory FOG assessment would enable non-pharmacologic support based on cues and would provide relevant information to neurologists on the disease evolution. This paper presents a method for FOG detection based on deep learning and signal processing techniques. This is, to the best of our knowledge, the first time that FOG detection is addressed with deep learning. The evaluation of the model has been done based on the data from 15 PD patients who manifested FOG. An inertial measurement unit placed at the left side of the waist recorded tri-axial accelerometer, gyroscope and magnetometer signals. Our approach achieved comparable results to the state-of-the-art, reaching validation performances of 88.6% and 78% for sensitivity and specificity respectively.Peer ReviewedPostprint (author's final draft

Design and evaluation of a medication application for people with Parkinson's disease

Smartphones may allow disease self-management, which is relevant for people with Parkinson’s Disease (PD) in need of frequent medication adjustments. Yet, there is little data on the interaction of people with PD with smartphones. We describe the processes of the design and usability evaluation of a smartphone medication application for PD. Results show that participants with PD were generally able to successfully interact with the tailored user interfaces (UI), and grasp navigation and organization principles designed into the application. The paper lists issues for UI improvement and further testing

User-centred design of a mobile self-management solution for parkinson's disease

Parkinson's disease (PD) is a highly prevalent and disabling condition, requiring frequent medication adjustments. In parallel, non-adherence to medical treatment might lead to severe consequences. Therefore, a solution to monitor PD symptoms, allowing neurologists to make informed decisions about medication adjustments, and one which could promote medical treatment adherence would be beneficial for both the patient and the medical doctor. In this paper we present the rationale and user-centred process for the design of four smartphone applications for the self-management of PD. We present the methods for evaluation and the results of usability tests. The results show that user-centred methods were efficient and that people with PD were able to achieve high task completion rates on usability tests with three of the applications for PD self-management. Future work should focus on detailed improvement of touch screen sensitivity to optimize error prevention

Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments

Freezing of gait (FoG) is one of the most disturbing and incapacitating symptoms in Parkinson's disease. It is defined as a sudden block in effective stepping, provoking anxiety, stress and falls. FoG is usually evaluated by means of different questionnaires; however, this method has shown to be not reliable, since it is subjective due to its dependence on patients’ and caregivers’ judgment. Several authors have analyzed the usage of MEMS inertial systems to detect FoG with the aim of objectively evaluating it. So far, specific methods based on accelerometer's frequency response has been employed in many works; nonetheless, since they have been developed and tested in laboratory conditions, their performance is commonly poor when being used at patients’ home. Therefore, this work proposes a new set of features that aims to detect FoG in real environments by using accelerometers. This set of features is compared with three previously reported approaches to detect FoG. The different feature sets are trained by means of several machine learning classifiers; furthermore, different window sizes are also evaluated. In addition, a greedy subset selection process is performed to reduce the computational load of the method and to enable a real-time implementation. Results show that the proposed method detects FoG at patients’ home with 91.7% and 87.4% of sensitivity and specificity, respectively, enhancing the results of former methods between a 5% and 11% and providing a more balanced rate of true positives and true negatives.Peer Reviewe

A waist-worn inertial measurement unit for Parkinson’s disease long-term monitoring

Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have demonstrated to accurately monitor motor symptoms of Parkinson’s disease (PD). In this sense, most of previous works have attempted to assess PD symptoms through IMUs in controlled environments or short tests. This paper presents the design of an IMU called 9x3 that aims to assess PD symptoms, enabling the possibility to perform a map of patients’ symptoms at their homes during long periods of time. The designed device is able to acquire and store raw inertial data for artificial intelligence algorithmic training purposes. Furthermore, the presented IMU also enables the real-time execution of the developed and embedded learning models. Results show the great flexibility of the 9x3, capable of storing inertial information and algorithm outputs, sending messages to external devices. This paper also presents the results of detecting freezing of gait and brad kinetic gait in 12 patients, with sensitivity and specificity above 80%. Additionally, the system enables working 23.09 days (at waking hours) with a 1200mAh battery sampling at 50 Hz, opening up the possibility to be employed at other applications like wellbeing and sports.Peer ReviewedPostprint (published version

Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit

Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may be the most incapacitating. FOG episodes may result in falls and reduce patients’ quality of life. Accurate assessment of FOG would provide objective information to neurologists about the patient’s condition and the symptom’s characteristics, while it could enable non-pharmacologic support based on rhythmic cues. This paper is, to the best of our knowledge, the first study to propose a deep learning method for detecting FOG episodes in PD patients. This model is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach was evaluated using data collected by a waist-placed inertial measurement unit from 21 PD patients who manifested FOG episodes. These data were also employed to reproduce the state-of-the-art methodologies, which served to perform a comparative study to our FOG monitoring system. The results of this study demonstrate that our approach successfully outperforms the state-of-the-art methods for automatic FOG detection. Precisely, the deep learning model achieved 90% for the geometric mean between sensitivity and specificity, whereas the state-of-the-art methods were unable to surpass the 83% for the same metric.Peer ReviewedPostprint (published version

A waist-worn inertial measurement unit for Parkinson’s disease long-term monitoring

Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have demonstrated to accurately monitor motor symptoms of Parkinson’s disease (PD). In this sense, most of previous works have attempted to assess PD symptoms through IMUs in controlled environments or short tests. This paper presents the design of an IMU called 9x3 that aims to assess PD symptoms, enabling the possibility to perform a map of patients’ symptoms at their homes during long periods of time. The designed device is able to acquire and store raw inertial data for artificial intelligence algorithmic training purposes. Furthermore, the presented IMU also enables the real-time execution of the developed and embedded learning models. Results show the great flexibility of the 9x3, capable of storing inertial information and algorithm outputs, sending messages to external devices. This paper also presents the results of detecting freezing of gait and brad kinetic gait in 12 patients, with sensitivity and specificity above 80%. Additionally, the system enables working 23.09 days (at waking hours) with a 1200mAh battery sampling at 50 Hz, opening up the possibility to be employed at other applications like wellbeing and sports.Peer Reviewe

Deep learning for detecting freezing of gait episodes in Parkinson’s disease based on accelerometers

The final publication is available at Springer via https://doi.org/10.1007/978-3-319-59147-6_30Freezing of gait (FOG) is one of the most incapacitating symptoms among the motor alterations of Parkinson’s disease (PD). Manifesting FOG episodes reduce patients’ quality of life and their autonomy to perform daily living activities, while it may provoke falls. Accurate ambulatory FOG assessment would enable non-pharmacologic support based on cues and would provide relevant information to neurologists on the disease evolution. This paper presents a method for FOG detection based on deep learning and signal processing techniques. This is, to the best of our knowledge, the first time that FOG detection is addressed with deep learning. The evaluation of the model has been done based on the data from 15 PD patients who manifested FOG. An inertial measurement unit placed at the left side of the waist recorded tri-axial accelerometer, gyroscope and magnetometer signals. Our approach achieved comparable results to the state-of-the-art, reaching validation performances of 88.6% and 78% for sensitivity and specificity respectively.Peer Reviewe