Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression, and medication response.

Parkinson's disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people's bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients' daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.

Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals.

There are currently no effective biomarkers for diagnosing Parkinson's disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson's Disease Rating Scale (R = 0.94, P = 3.6 × 10-25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person's body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis.

Monitoring Behaviors of Patients With Late-Stage Dementia Using Passive Environmental Sensing Approaches: A Case Series.

OBJECTIVE: To show the feasibility of using different unobtrusive activity-sensing technologies to provide objective behavioral markers of persons with dementia (PwD). DESIGN: Monitored the behaviors of two PwD living in memory care unit using the Oregon Center for Aging & Technology (ORCATECH) platform, and the behaviors of two PwD living in assisted living facility using the Emerald device. SETTING: A memory care unit in Portland, Oregon and an assisted living facility in Framingham, Massachusetts. PARTICIPANTS: A 63-year-old male with Alzheimer's disease (AD), and an 80-year-old female with frontotemporal dementia, both lived in a memory care unit in Portland, Oregon. An 89-year-old woman with a diagnosis of AD, and an 85-year-old woman with a diagnosis of major neurocognitive disorder, Alzheimer's type with behavioral symptoms, both resided at an assisted living facility in Framingham, Massachusetts. MEASUREMENTS: These include: sleep quality measured by the bed pressure mat; number of transitions between spaces and dwell times in different spaces measured by the motion sensors; activity levels measured by the wearable actigraphy device; and couch usage and limb movements measured by the Emerald device. RESULTS: Number of transitions between spaces can identify the patient's episodes of agitation; activity levels correlate well with the patient's excessive level of agitation and lack of movement when the patient received potentially inappropriate medication and neared the end of life; couch usage can detect the patient's increased level of apathy; and periodic limb movements can help detect risperidone-induced side effects. This is the first demonstration that the ORCATECH platform and the Emerald device can measure such activities. CONCLUSION: The use of technologies for monitoring behaviors of PwD can provide more objective and intensive measurements of PwD behaviors.

Contactless In-Home Monitoring of the Long-Term Respiratory and Behavioral Phenotypes in Older Adults With COVID-19: A Case Series.

Currently, there is a limited understanding of long-term outcomes of COVID-19, and a need for in-home measurements of patients through the whole course of their disease. We study a novel approach for monitoring the long-term trajectories of respiratory and behavioral symptoms of COVID-19 patients at home. We use a sensor that analyzes the radio signals in the room to infer patients' respiration, sleep and activities in a passive and contactless manner. We report the results of continuous monitoring of three residents of an assisted living facility for 3 months, through the course of their disease and subsequent recovery. In total, we collected 4,358 measurements of gait speed, 294 nights of sleep, and 3,056 h of respiration. The data shows differences in the respiration signals between asymptomatic and symptomatic patients. Longitudinally, we note sleep and motor abnormalities that persisted for months after becoming COVID negative. Our study represents a novel phenotyping of the respiratory and behavioral trajectories of COVID recovery, and suggests that the two may be integral components of the COVID-19 syndrome. It further provides a proof-of-concept that contactless passive sensors may uniquely facilitate studying detailed longitudinal outcomes of COVID-19, particularly among older adults.

Assessment of medication self-administration using artificial intelligence.

Errors in medication self-administration (MSA) lead to poor treatment adherence, increased hospitalizations and higher healthcare costs. These errors are particularly common when medication delivery involves devices such as inhalers or insulin pens. We present a contactless and unobtrusive artificial intelligence (AI) framework that can detect and monitor MSA errors by analyzing the wireless signals in the patient's home, without the need for physical contact. The system was developed by observing self-administration conducted by volunteers and evaluated by comparing its prediction with human annotations. Findings from this study demonstrate that our approach can automatically detect when patients use their inhalers (area under the curve (AUC) = 0.992) or insulin pens (AUC = 0.967), and assess whether patients follow the appropriate steps for using these devices (AUC = 0.952). The work shows the potential of leveraging AI-based solutions to improve medication safety with minimal overhead for patients and health professionals.

Novel Technology to Capture Objective Data from Patients' Recovery from Laparoscopic Endometriosis Surgery.

STUDY OBJECTIVE: To assess the feasibility of a noncontact radio sensor as an objective measurement tool to study postoperative recovery from endometriosis surgery. DESIGN: Prospective cohort pilot study. SETTING: Center for minimally invasive gynecologic surgery at an academically affiliated community hospital in conjunction with in-home monitoring. PATIENTS: Patients aged above 18 years who sleep independently and were scheduled to have laparoscopy for the diagnosis and treatment of suspected endometriosis. INTERVENTIONS: A wireless, noncontact sensor, Emerald, was installed in the subjects' home and used to capture physiologic signals without body contact. The device captured objective data about the patients' movement and sleep in their home for 5 weeks before surgery and approximately 5 weeks postoperatively. The subjects were concurrently asked to complete a daily pain assessment using a numeric rating scale and a free text survey about their daily symptoms. MEASUREMENTS AND MAIN RESULTS: Three women aged 23 years to 39 years and with mild to moderate endometriosis participated in the study. Emerald-derived sleep and wake times were contextualized and corroborated by select participant comments from retrospective surveys. In addition, self-reported pain levels and 1 sleep variable, sleep onset to deep sleep time, showed a significant (p <.01), positive correlation with next-day-pain scores in all 3 subjects: r = 0.45, 0.50, and 0.55. In other words, the longer it took the subject to go from sleep onset to deep sleep, the higher their pain score the following day. CONCLUSION: A patient's experience with pain is challenging to meaningfully quantify. This study highlights Emerald's unique ability to capture objective data in both preoperative functioning and postoperative recovery in an endometriosis population. The utility of this uniquely objective data for the clinician-patient relationship is just beginning to be explored.

Deep Phenotyping of Parkinson's Disease.

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.

Radio Signal Sensing and Signal Processing to Monitor Behavioral Symptoms in Dementia: A Case Study.

OBJECTIVES: Alzheimer's Disease (AD)-related behavioral symptoms (i.e. agitation and/or pacing) develop in nearly 90% of AD patients. In this N = 1 study, we provide proof-of-concept of detecting changes in movement patterns that may reflect underlying behavioral symptoms using a highly novel radio sensor and identifying environmental triggers. METHODS: The Emerald device is a Wi-Fi-like box without on-body sensors, which emits and processes radio-waves to infer patient movement, spatial location and activity. It was installed for 70 days in the room of patient 'E', exhibiting agitated behaviors. RESULTS: Daily motion episode aggregation revealed motor activity fluctuation throughout the data collection period which was associated with potential socio-environmental triggers. We did not detect any adverse events attributable to the use of the device. CONCLUSION: This N-of-1 study suggests the Emerald device is feasible to use and can potentially yield actionable data regarding behavioral symptom management. No active or potential device risks were encountered.

Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?

Goal: The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed non-infected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.

Passive Monitoring at Home: A Pilot Study in Parkinson Disease.

We conducted a pilot study using a passive radio-wave-based home monitor in individuals with Parkinson disease (PD) with a focus on gait, home activity, and time in bed. We enrolled 7 ambulatory individuals to have the device installed in the bedroom of their homes over 8 weeks and performed standard PD assessments at baseline. We evaluated the ability of the device to objectively measure gait and time in bed and to generate novel visualizations of home activity. We captured 353 days of monitoring. Mean gait speed (0.39-0.78 m/s), time in bed per day (4.4-12.1 h), and number (1.4-5.9) and duration (15.0-49.8 min) of nightly awakenings varied substantially across and within individuals. Derived gait speed correlated well with the Movement Disorder Society-Unified Parkinson's Disease Rating Scale total (r = -0.88, p = 0.009) and motor sub-score (r = -0.95, p = 0.001). Six of the seven participants agreed that their activity was typical and indicated a willingness to continue monitoring. This technology provided promising new insights into the home activities of those with PD and may be broadly applicable to other chronic conditions.

Fast multi-dimensional NMR acquisition and processing using the sparse FFT.

Increasing the dimensionality of NMR experiments strongly enhances the spectral resolution and provides invaluable direct information about atomic interactions. However, the price tag is high: long measurement times and heavy requirements on the computation power and data storage. We introduce sparse fast Fourier transform as a new method of NMR signal collection and processing, which is capable of reconstructing high quality spectra of large size and dimensionality with short measurement times, faster computations than the fast Fourier transform, and minimal storage for processing and handling of sparse spectra. The new algorithm is described and demonstrated for a 4D BEST-HNCOCA spectrum.