The use of wearables for the diagnosis and treatment of Parkinson’s disease

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, with increasing numbers of affected patients. Many patients lack adequate care due to insufficient specialist neurologists/geriatricians, and older patients experience difficulties traveling far distances to reach their treating physicians. A new option for these obstacles would be telemedicine and wearables. During the last decade, the development of wearable sensors has allowed for the continuous monitoring of bradykinesia and dyskinesia. Meanwhile, other systems can also detect tremors, freezing of gait, and gait problems. The most recently developed systems cover both sides of the body and include smartphone apps where the patients have to register their medication intake and well-being. In turn, the physicians receive advice on changing the patient’s medication and recommendations for additional supportive therapies such as physiotherapy. The use of smartphone apps may also be adapted to detect PD symptoms such as bradykinesia, tremor, voice abnormalities, or changes in facial expression. Such tools can be used for the general population to detect PD early or for known PD patients to detect deterioration. It is noteworthy that most PD patients can use these digital tools. In modern times, wearable sensors and telemedicine open a new window of opportunity for patients with PD that are easy to use and accessible to most of the population.

Evaluating Ontology-Based PD Monitoring and Alerting in Personal Health Knowledge Graphs and Graph Neural Networks

“…Specifically, in the context of PD, a well-defined ontology, such as the Wear4PDmove ontology, plays a pivotal role in ensuring precision and specificity in representing and reasoning with PD-related data. The semantic richness afforded by these ontologies not only aids in the interoperability of diverse health data sources but also forms the basis for rule-based event recognition, enabling timely and contextually relevant medical interventions [25]. As wearable sensor technology becomes increasingly prevalent in health monitoring, the synergy of ontologically structured data within a PHKG stands as a promising paradigm for tailoring healthcare insights to the unique nuances of an individual's health journey [2].…”

Wearable Sensors in Other Medical Domains with Application Potential for Orthopedic Trauma Surgery—A Narrative Review

“…Wearable sensors have been investigated for the diagnosis and measurement of disease progression in NDDs [ 58 , 59 , 60 ]. Moreover, with increasing incidence and a shortage of physician specialists, the treatment of patients with NDDs is becoming increasingly difficult, so the use of telemedicine and wearable devices may provide an alternative perspective to appropriate care [ 61 ]. In Parkinson’s disease, for example, smartphone apps can be used to diagnose fine motor impairment in an early stage based on typing characteristics on a touchscreen [ 62 ].…”

“…In Parkinson’s disease, for example, smartphone apps can be used to diagnose fine motor impairment in an early stage based on typing characteristics on a touchscreen [ 62 ]. A specially developed app accessible on a smartphone can also analyze changes in facial expressions based on a self-portrait photograph, examine emotional status based on text messages, and examine speech and movement patterns [ 61 ].…”

Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies

“…Likewise, the virtual keyboards in smartphones have been used to identify hallmark signatures of PD through analysis of variables such as stroke pressure and interkey flight/dwell time in addition to specific aspects of what is typed, the frequency of such, and the error rates within [ 50 ]. A rapidly emerging tool is the use of the front camera for the analysis of features and idiosyncrasies in PD, which includes saccadic movement of the eye [ 51 ] and signature movements of the facial muscles, including the lips [ 52 ]. Each of these four classes of signals are powerful in their own right, and could form a time stamp of baseline to current to assess time-lapsed disease progression and the impact of therapeutics and P4 medicine interventions longitudinally, potentially over decades.…”