Deep learning Parkinson's from smartphone data

Abstract

The cloudUPDRS app is a Class I medical device, namely an active transient non-invasive instrument, certified by the Medicines and Healthcare products Regulatory Agency in the UK for the clinical assessment of the motor symptoms of Parkinson's Disease. The app follows closely the Unified Parkinson's Disease Rating Scale which is the most commonly used protocol in the clinical study of PD; can be used by patients and their carers at home or in the community; and, requires the user to perform a sequence of iterated movements which are recorded by the phone sensors. This paper discusses how the cloudUPDRS system addresses two key challenges towards meeting essential consistency and efficiency requirements, namely: (i) How to ensure high-quality data collection especially considering the unsupervised nature of the test, in particular, how to achieve firm user adherence to the prescribed movements; and (ii) How to reduce test duration from approximately 25 minutes typically required by an experienced patient, to below 4 minutes, a threshold identified as critical to obtain significant improvements in clinical compliance. To address the former, we combine a bespoke design of the user experience tailored so as to constrain context, with a deep learning approach used to identify failures to follow the movement protocol while at the same time limiting false positives to avoid unnecessary repetition. We address the latter by developing a machine learning approach to personalise assessments by selecting those elements of the UPDRS protocol that most closely match individual symptom profiles and thus offer the highest inferential power hence closely estimating the patent's overall UPRDS score. • Personalised tests reducing the time required to carry out an assessment to less than 4 minutes. These so-called quick tests are created using machine learning to select a subset of UPDRS observations that closely estimate the motor performance of a particular patient.

The cloudUPDRS app: A medical device for the clinical assessment of Parkinson’s Disease

“…Figure 6) to solve the classification problem. The work presented in this paper, extends [57] in two significant ways. First, the software has been re-implemented from first principles using Tensor-Flow [2] so that it can be fully incorporated into the app running on the mobile device rather than be applied at the service back-end.…”

“…Second, although we obtained good performance using the approach presented in [57], the specific neural network architecture employed examines only a segment around the mid-section of the recorded signal (cf.…”

“…There are two main observations that guided the development of a new architecture to replace the work presented in [57]. First, in [57] each individual axis of acceleration is considered separately; that is four separate DMLPs are generated and trained for each of the x, y and z coordinates as well as the derived feature m representing the magnitude of acceleration (cf. Figure 6).…”

“…In this paper, we extend the work discussed in [57] to present two distinctive features of cloudUPDRS developed to address specific challenges related with care for PD patients, namely:…”

Deep Learning in Mobile and Wireless Networking: A Survey

“…Ordóñez and Roggen architect an advanced ConvLSTM to fuse data gathered from multiple sensors and perform activity recognition [112]. By leveraging CNN and LSTM structures, ConvLSTMs can automatically compress spatio-temporal sensor data into low-dimensional [236] Mobile ear Edge-based CNN Jindal [237] Heart rate prediction Cloud-based DBN Kim et al [238] Cytopathology classification Cloud-based CNN Sathyanarayana et al [239] Sleep quality prediction Cloud-based MLP, CNN, LSTM Li and Trocan [240] Health conditions analysis Cloud-based Stacked AE Hosseini et al [241] Epileptogenicity localisation Cloud-based CNN Stamate et al [242] Parkinson's symptoms management Cloud-based MLP Quisel et al [243] Mobile health data analysis Cloud-based CNN, RNN Khan et al [244] Respiration [250] Facial recognition Cloud-based CNN Wu et al [291] Mobile visual search Edge-based CNN Rao et al [251] Mobile augmented reality Edge-based CNN Ohara et al [290] WiFi-driven indoor change detection Cloud-based CNN,LSTM Zeng et al [252] Activity recognition Cloud-based CNN, RBM Almaslukh et al [253] Activity recognition Cloud-based AE Li et al [254] RFID-based activity recognition Cloud-based CNN Bhattacharya and Lane [255] Smart watch-based activity recognition Edge-based RBM Antreas and Angelov [256] Mobile surveillance system Edge-based & Cloud based CNN Ordóñez and Roggen [112] Activity recognition Cloud-based ConvLSTM Wang et al [257] Gesture recognition Edge-based CNN, RNN Gao et al [258] Eating detection Cloud-based DBM, MLP Zhu et al [259] User energy expenditure estimation Cloud-based CNN, MLP Sundsøy et al [260] Individual income classification Cloud-based MLP Chen and Xue [261] Activity recognition Cloud-based CNN Ha and Choi [262] Activity recognition Cloud-based CNN Edel and Köppe [263] Activity recognition Edge-based Binarized-LSTM Okita and Inoue [266] Multiple overlapping activities recognition Cloud-based CNN+LSTM Alsheikh et al…”