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Introduction Sleep disruption, and in particular decreased NREM stage N3 sleep, has been implicated in cognition and pathogenesis of Alzheimer's Disease (AD). Several recent findings suggest that enhancing N3 sleep may mitigate impairments due to chronic sleep disruption. However, approaches such as transcranial electrical stimulation (TES) have been limited by discomfort, awakenings, and lack of blinding. In response to the need to develop low-level TES, our team first identified the source of slow oscillations (SOs) of N3 sleep using high-density EEG array modeling (Sourcerer®), which localized within anterior limbic brain structures. Next, we showed that low-level TES targeted to this region induced and enhanced SOs during N3 sleep in the laboratory. More recently, we developed a portable TES device with the minimum deployable set of stimulation electrodes targeting the anterior limbic SO-generating regions: Wireless Interface Sensor Pod (WISP). Methods Thirteen healthy adults (m = 42 years) participated in three all-night sleep EEG recordings where they received low level (0.5 mA) TES designed to target anterior limbic SO-generating regions and a sham stimulation (placebo). TES was initiated after 4 minutes of naturally occurring NREM N2 sleep and included five TES blocks of five minutes each separated by one minute rest periods. Subjects were blinded to stimulation and sham conditions, and outcomes included feasibility, acceptability, and total time in each sleep stage. A convolutional neural network (CNN) was developed, trained, and tested on manually scored EEG sleep staging for automated sleep scoring. Results Subjects rated that the WISP device was highly feasible and acceptable. Subjects were adequately blinded to sham versus stimulation. When compared to the sham session, limbic-targeted TES significantly increased the duration of N3 sleep and spectral power in the 0.5-1 Hz frequency band. Finally, our CNN was able to detect sleep stages with good accuracy (84%, kappa 0.77). Conclusion These results suggest that low-level portable TES using a minimum deployable set of electrodes, when specifically targeting anterior limbic sites, is feasible, acceptable, maintains blinding, and increases deep (N3) sleep. The WISP device is a promising method to both assess and manipulate sleep in the home setting. Support (if any) DoD MOMRP MTEC-21-06-MPAI-129

Juan Piantino,1 Madison Luther,1 Christina Reynolds,2 Miranda M Lim2– 4 1Department of Pediatrics, Division of Child Neurology, Doernbecher Children’s Hospital, Oregon Health and Science University, Portland, OR, USA; 2Department of Neurology, Department of Medicine, Division of Pulmonary and Critical Care Medicine, Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA; 3Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, USA; 4Neurology Research Service and National Center for Rehabilitative Auditory Research, VA Portland Health Care System, Portland, OR, USACorrespondence: Juan Piantino Email piantino@ohsu.eduPurpose: Wrist-worn actigraphy via research-grade devices, a well-established approach to the assessment of rest-activity, is limited by poor compliance, battery life, and lack of direct evidence for time spent physically in the bed. A non-invasive bed sensor (Emfit) may provide advantages over actigraphy for long-term sleep assessment in the home. This study compared sleep-wake measurements between this sensor and a validated actigraph.Patients and Methods: Thirty healthy subjects (6 to 54 years) underwent simultaneous monitoring with both devices for 14 days and filled out a daily sleep diary. Parameters included bed entry time, sleep start, sleep end, bed exit time, rest interval duration, and wake after sleep onset (WASO). The agreement between the two devices was measured using Bland–Altman plots and inter-class correlation coefficients (ICC). In addition, sensitivity, specificity, and accuracy were obtained from epoch-by-epoch comparisons of Emfit and actigraphy.Results: Fifteen percent of the subjects reported that wearing the actigraph was a burden. None reported that using the bed sensor was a burden. The minimal detectable change between Emfit and actigraphy was 11 minutes for bed entry time, 14 minutes for sleep start, 14 minutes for sleep end, 10 minutes for bed exit time, 20 minutes for rest interval duration, and 110 minutes for WASO. Inter-class correlation coefficients revealed an excellent agreement for all sleep parameters (ICC=0.99, 95% CI 98– 99) except for WASO (ICC=0.46, 95% CI 0.33– 0.56). Sensitivity, specificity, and accuracy were 0.62, 0.93, and 0.88, respectively. Kappa correlation analysis revealed a moderate correlation between the two devices (κ=0.55, p< 0.0001).Conclusion: Emfit is an acceptable alternative to actigraphy for the estimation of bed entry time, sleep start, sleep end, bed exit time, and rest interval duration. However, WASO estimates are poorly correlated between the two devices. Emfit may offer methodological advantages in situations where actigraphy is challenging to implement.Keywords: Actiwatch 2, Emfit, ambulatory, sleep-wake monitoring

Mobility features are important for health. In this study, we quantified in-home mobility and explored potential health and environmental correlates. Participants included community-dwelling older adults living alone (n=139). Two indoor mobility features were developed (frequency; interdaily stability) using passive, room-level (bathroom; bedroom; kitchen; living room) infrared motion sensors, and contact sensors attached to homes’ egress doors at entryways. Existing data on gait and cognition, weekly online surveys assessing mood (blueness; loneliness) and pain severity, and publicly available locality-based atmospheric (daylight hours) data were leveraged. On average, higher indoor mobility frequency was associated with faster gait speed (p=0.04), less time out-of-home (

Background and Objectives The Remote Assessment and Dynamic Response (READyR) Program was developed in order to address the current lack of early-stage dementia care planning programs that assess the care needs of persons with dementia. The goal was to create a program informed by care values and ongoing ecologically valid data. The objectives of this study are to describe the development and design process of the READyR Program, and to evaluate the utility of the READyR Program for identifying dementia-related care needs. Research Design and Methods A prototype of the web-based READyR Program tool was first created using digital activity data that were collected by previous studies using a platform of multimodal sensors installed in the homes of older adult couples with and without dementia. Digital activity data were then mapped onto potential care values (e.g., safety & autonomy) to create a values-based needs assessment that is tailored to the individual care dyad. Next, evaluation of the READyR Program by 11 professional dementia care coordinators and case managers (across 3 semistructured focus groups) was used to explore the utility of READyR for assessing dementia-related needs. Qualitative description using conventional content analysis was used to iteratively code focus group data and to describe prevalent themes. Results Prevalent focus groups themes included barriers to (e.g., family relationship strain) and facilitators of (e.g., tailored assessments) the optimal process for assessing dementia-related care needs by care coordinators, as well as advantages to (e.g., providing new objective insights into function, and routines) and disadvantages of (e.g., bringing up new questions about care) incorporating the remote monitoring data into a values-based needs assessment. Discussion and Implications READyR has the potential to help family members, as well as care coordinators and providers, gain insight into the values-based care needs of persons with early-stage dementia. Clinical Trials Registration Number: NCT04542109

Introduction Prospective studies of Alzheimer’s disease (AD) demonstrate sleep-wake disturbances may precede and accelerate with cognitive decline. Use of wrist-actigraphy in conjunction with overnight polysomnography (PSG) enables researchers to evaluate diverse characteristics of sleep and circadian rhythms. Though outcome measures from these devices have been implicated in the progression of AD, because of great individual differences, it is valuable to internally replicate within cohort. Here we demonstrate application of actigraphy in the context of healthy aging to identify patterns of activity that predict cognitive decline. Methods Participants were prospectively recruited at Washington University as part of the BASE study and wore an Actiwatch2 (Philips-Respironics) and cross-validated for sleep and wake using polysomnography. The watch was worn continuously for at least 5 consecutive nights to be included in the analysis (n = 68, age = 71.1 ± 4.5 years). Rest-activity rhythms were computed using compiled code from National Sleep Research Resource (actiCircadian) pipeline implemented in MATLAB R2021a (Mathworks). Clinical Dementia Rating (CDR) scales and cognitive tests were conducted on the first day of actigraphy recording. Results Individuals with cognitive impairment (CDR Global Score > 0) had increased wake after sleep onset (WASO) and reduced sleep efficiency compared with individuals with no impairment (CDR = 0). (unpaired ttest: p = 0.02 and p = 0.02). Participants with cognitive impairment had a trend toward reduced relative amplitude (RA) (p = 0.06). Reduced (worse) RA was correlated with worse performance on the Trail Making Task A (Pearson r: -0.37, p = 0.002). Higher (better) interdaily Stability (IS) was associated with better responses on subjective questionnaires including Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index (Pearson r: -0.24 p = 0.051; r: 0.242, p = 0.047, respectively). Conclusion Actigraphically-derived rest-activity metrics are correlated with cognitive status, cognitive test performance, and subjective sleep questionnaires. Support (If Any) NIH R01 AG059507, ARCS Foundation Scholar

Introduction There exists a bidirectional relationship between sleep disruption and neuropathology in Alzheimer’s disease (AD). Electroencephalogram (EEG) during polysomnography (PSG) provides an opportunity to examine stereotyped, coordinated brain activity. Specifically, slow wave activity (SWA), a defining feature of non-REM sleep, is aberrant in AD, and disruption of SWA in healthy adults is related to increased amyloid-beta levels. Coherence of slow waves across different cortical regions may serve as a metric of brain network coordination, sensitive to earliest AD pathology. We explored slow wave coherence during sleep in relation to sleep macrostructure and cognitive performance in the Biomarkers of Alzheimer’s Disease in Sleep and EEG(BASE) cohort. Methods EEG was collected during an attended overnight PSG from the BASE cohort (n=79, average age = 70.8 ± 4.4 years), approximately 20% of whom had Clinical Dementia Rating (CDR) of 0.5-1. A custom slow wave peak detector was implemented in MATLAB to across 6 EEG leads (C3, C4, F3, F4, O1, and O2), and slow wavecoherence was calculated as the proportion of sleep with slow waves occurring within 100ms in all 6 leads. Results EEG slow wave coherence was reliably quantified during wake, non-REM stages N1, N2, N3, and REM. Slow wave coherence in N2/N3 was negatively correlated with measures of sleep fragmentation (wake after sleep onset Spearman r = -0.3, p 0.008; number of awakenings r = -0.39, p 0.0004), and positively correlated with sleep efficiency (r = 0.28, p = 0.013). Slow wave coherence in N2/N3 was correlated with overall cognition as measured by MoCA adjusted z-score (r = 0.2, p 0.087) and executive function as measured by Trailmaking B adjusted z-score (r = 0.25, p 0.038). Conclusion Slow wave coherence was strongly correlated with measures of sleep quality. Reduced slow wave coherence was associated with poorer cognitive executive function. EEG slow wave coherence is a novel, promising approach to measure coordinated brain network function that is sensitive to early cognitive dysfunction in AD. Support (If Any) NIH R01 AG059507, ARCS Foundation Scholar

Introduction Chronic pain and sleep disturbances are intricately linked to one another, especially in individuals with a history of traumatic brain injury (TBI) who are at greater risk for both symptoms. Although prior studies have analyzed differences in sleep electroencephalogram (EEG) in these clinical populations, the association between sleep EEG slow wave coherence and pain complaints is not fully examined or known. Our novel slow wave coherence approach may provide new insights into the relationship between TBI, chronic pain, and sleep Methods Ninety-six veterans were recruited and enrolled under a VA IRB-approved protocol. Participants completed a semi-structured clinical interview to determine their history of TBI, Symptom Impact Questionnaire Revised (SIQR), a measure of chronic pain complaints, and underwent an attended overnight in-lab polysomnogram (PSG). We developed a novel computational signal processing algorithm to identify and quantify EEG slow waves within 100 ms bins across the 6 standard PSG EEG channels. When a slow wave was simultaneously observed in 4 or more of the 6 leads, slow wave coherence was inferred, and a percentage of slow wave coherence across each of the sleep stages was then calculated for each subject. Results In our sample, 65 participants (67.7%) endorsed experiencing chronic pain lasting 3 months or longer, and 54 had a history of TBI (56.3%). Participants endorsing chronic pain had a significantly lowered percent of EEG slow wave coherence during NREM sleep than subjects without chronic pain (p = 0.01). NREM EEG slow wave coherence did not correlate with SIQR scores in subjects without TBI (r = -0.03, p = 0.90), but was significantly negatively correlated in subjects with TBI (r = -0.32, p = 0.02). Conclusion EEG slow wave coherence during NREM sleep is correlated with chronic pain complaints in Veterans with a history of TBI, and could be indicative of neuronal dysfunction during sleep. Further research on slow wave coherence is warranted to understand the underlying mechanisms for the association between chronic pain and poor sleep following TBI. Support (if any) D01 W81XWH-17-1-0423

Introduction Glutamate concentrations in the cortex fluctuate with the sleep wake cycle in both rodents and humans. Altered glutamatergic signaling, as well as the early life onset of sleep disturbances have been implicated in neurodevelopmental disorders such as autism spectrum disorder. In order to study how sleep modulates glutamate activity in brain regions relevant to social behavior and development, we disrupted sleep in the socially monogamous prairie vole (Microtus ochrogaster) rodent species and quantified markers of glutamate neurotransmission within the prefrontal cortex, an area of the brain responsible for advanced cognition and complex social behaviors. Methods Male and female prairie voles were sleep disrupted using an orbital shaker to deliver automated gentle cage agitation at continuous intervals. Sleep was measured using EEG/EMG signals and paired with real time glutamate concentrations in the prefrontal cortex using an amperometric glutamate biosensor. This same method of sleep disruption was applied early in development (postnatal days 14–21) and the long term effects on brain development were quantified by examining glutamatergic synapses in adulthood. Results Consistent with previous research in rats, glutamate concentration in the prefrontal cortex increased during periods of wake in the prairie vole. Sleep disruption using the orbital shaker method resulted in brief cortical arousals and reduced time in REM sleep. When applied during development, early life sleep disruption resulted in long-term changes in both pre- and post-synaptic components of glutamatergic synapses in the prairie vole prefrontal cortex including increased density of immature spines. Conclusion In the prairie vole rodent model, sleep disruption on an orbital shaker produces a sleep, behavioral, and neurological phenotype that mirrors aspects of autism spectrum disorder including altered features of excitatory neurotransmission within the prefrontal cortex. Studies using this method of sleep disruption combined with real time biosensors for excitatory neurotransmitters will enhance our understanding of modifiable risk factors, such as sleep, that contribute to the altered development of glutamatergic synapses in the brain and their relationship to social behavior. Support (if any) NSF #1926818, VA CDA #IK2 BX002712, Portland VA Research Foundation, NIH NHLBI 5T32HL083808-10, VA Merit Review #I01BX001643

Introduction: Future digital health research hinges on methodologies to conduct remote clinical assessments and in-home monitoring. The Collaborative Aging Research Using Technology (CART) initiative was introduced to establish a digital technology research platform that could widely assess activity in the homes of diverse cohorts of older adults and detect meaningful change longitudinally. This paper reports on the built end-to-end design of the CART platform, its functionality, and the resulting research capabilities. Methods: CART platform development followed a principled design process aiming for scalability, use case flexibility, longevity, and data privacy protection while allowing sharability. The platform, comprising ambient technology, wearables, and other sensors, was deployed in participants’ homes to provide continuous, long-term (months to years), and ecologically valid data. Data gathered from CART homes were sent securely to a research server for analysis and future data sharing. Results: The CART system was created, iteratively tested, and deployed to 232 homes representing four diverse cohorts (African American, Latinx, low-income, and predominantly rural-residing veterans) of older adults (n = 301) across the USA. Multiple measurements of wellness such as cognition (e.g., mean daily computer use time = 160–169 min), physical mobility (e.g., mean daily transitions between rooms = 96–155), sleep (e.g., mean nightly sleep duration = 6.3–7.4 h), and level of social engagement (e.g., reports of overnight visitors = 15–45%) were collected across cohorts. Conclusion: The CART initiative resulted in a minimally obtrusive digital health-enabled system that met the design principles while allowing for data capture over extended periods and can be widely used by the research community. The ability to monitor and manage health digitally within the homes of older adults is an important alternative to in-person assessments in many research contexts. Further advances will come with wider, shared use of the CART system in additional settings, within different disease contexts, and by diverse research teams.

Indoor room transition is an underexplored real-world activity outcome. We estimated the stability and variability of indoor room transitions and their associations with mild cognitive impairment (MCI) in older adults. Older adults living-alone (n=159, age=78.3±8.8 years, 14% MCI) from the Oregon Center for Aging & Technology (ORCATECH) and the Minority Aging Research Study (MARS) were included. Room transitions were detected using passive infrared motion sensors in bathroom, bedroom, kitchen, and living room. The hourly number of room transitions was used to calculate the interdaily stability and intradaily variability of room transitions. MCI was operationalized by the Clinical Dementia Rating equaled 0.5. Generalized estimating equations models adjusted for demographics, health, and environmental factors revealed that older adults with MCI had a lower interdaily stability of room transitions than cognitive healthy peers (z=-2.06,p=0.03). A pervasive-sensing system deployed in homes can obtrusively measure room transition activities to inform cognitive health in older adults.

Introduction Sleep disturbances are common in elderly patients and may contribute to disease progression in certain populations (e.g., Alzheimer’s Disease). Light therapy is a simple and cost-effective intervention to improve sleep. Primary barriers to light therapy are 1) poor acceptability to use of devices and 2) inflexibility of current devices to deliver beyond a fixed spectrum and throughout the entirety of the day. However, dynamic, tunable lighting integrated into the native home lighting system can maximize short-wavelength light in the morning and minimize short-wavelength light in the evening, thus entraining circadian rhythms and treating sleep disturbances, and overcome these limitations. We determined the feasibility of implementing a whole-home tunable lighting system as a potential sleep intervention. Methods Tunable LED lights were installed throughout the homes of healthy older adults already enrolled in an existing study with embedded home assessment platforms (ORCATECH study; n=4 subjects in n=3 homes). In ORCATECH, continuous data on room location, activity, sleep, and general health parameters are collected at minute-to-minute resolution over months to years of participation. This single arm longitudinal design collected participants’ light usage in addition to ORCATECH outcome measures. Primary outcomes for this pilot study included the feasibility and patient acceptability. Exploratory outcomes were sleep metrics (sleep time, latency, efficiency), mobility (room transitions and actigraphy), and overall health indices (weekly body weight, self-report general health questionnaires) both pre- and post-intervention. Results Two subjects terminated the study citing technical difficulties with the lights and a preference for brighter illumination. Of the remaining 2 participants, sleep metrics were explored over a 12-month period spanning pre- and post-installation of lights. Nightly duration in bed was compared with minute-to-minute room entry data and actigraphy with high inter-measure reliability. Conclusion These data support that tunable whole-home lighting systems are reasonably acceptable and feasibly implemented using an automated platform for continuous data collection. Quantification of sleep over long periods of time is robust and reliable in the home environment of elderly subjects. These results will inform implementation of future large-scale lighting intervention studies in patients at risk for developing Alzheimer’s Disease. Support (if any) Hartford Gerontological Center Interprofessional Award, Pacific Northwest National Laboratory, OHSU ORCATECH

Introduction A bidirectional relationship exists between sleep disruption and neuropathology in Alzheimer’s disease (AD). The sleep electroencephalogram (EEG) is a highly stereotyped, direct neurophysiological window into brain function; prior studies have identified abnormalities in EEG slow waves in early AD. EEG coherence across channels during sleep, a normally highly coherent brain state, could be an indicator of network coordination across brain regions. Accordingly, altered slow wave coherence during sleep may be an early indicator of cognitive decline. Methods EEG was collected during an attended overnight polysomnogram (PSG) from a community-based cohort of older subjects (n=44, average age = 71), approximately 25% of whom met criteria for mild cognitive impairment or early AD. Files were exported to EDF and a slow wave peak detector was implemented in MATLAB to count the number of slow wave oscillations, with automated artifact rejection, across 6 EEG leads standard for PSG (C3, C4, F3, F4, O1, and O2). Slow wave coherence was inferred when slow waves occurred in temporal synchrony across channels within 100 ms. Results Subjects with cognitive impairment showed significantly reduced total sleep time and time spent in rapid eye movement (REM) sleep compared to age-matched controls. EEG slow wave coherence was reliably quantified during wake, non-REM stages N1, N2, N3, and REM vigilance states as well as during transition periods between sleep stages. Using this algorithm, specific signatures of slow wave propagation during sleep were identified, including increased variability in slow wave activity and coherence, that appeared more prominent in subjects with impaired cognition. Conclusion EEG slow wave coherence during sleep and wake states can be calculated by applying automated algorithms to PSG data, and may be associated with cognitive impairment. Support (if any) NIH R01 AG059507

The information processing capability of the brain decreases during unconscious states. Capturing this decrease during anesthesia-induced unconsciousness has been attempted using standard spectral analyses as these correlate relatively well with breakdowns in corticothalamic networks. Much of this work has involved the use of propofol to perturb brain activity, as it is one of the most widely used anesthetics for routine surgical anesthesia. Propofol administration alone produces EEG spectral characteristics similar to most hypnotics; however, inter-individual and drug variation render spectral measures inconsistent. Complexity measures of EEG signals could offer better measures to distinguish brain states, because brain activity exhibits nonlinear behavior at several scales during transitions of consciousness. We tested the potential of complexity analyses from nonlinear dynamics to identify loss and recovery of consciousness at clinically relevant timepoints. Patients undergoing propofol general anesthesia for various surgical procedures were identified as having changes in states of consciousness by the loss and recovery of response to verbal stimuli after induction and upon cessation of anesthesia, respectively. We demonstrate that nonlinear dynamics analyses showed more significant differences between consciousness states than spectral measures. Notably, attractors in conscious and anesthesia-induced unconscious states exhibited significantly different shapes. These shapes have implications for network connectivity, information processing, and the total number of states available to the brain at these different levels. They also reflect some of our general understanding of the network effects of consciousness in a way that spectral measures cannot. Thus, complexity measures could provide a universal means for reliably capturing depth of consciousness based on EEG changes at the beginning and end of anesthesia administration.

Propofol is one of the most widely used anesthetics for routine surgical anesthesia. Propofol administration alone produces EEG spectral characteristics similar to most hypnotics; however, inter-individual variation can make spectral measures inconsistent. Complexity measures of EEG signals could offer universal measures to better capture anesthetic depth as brain activity exhibits nonlinear behavior at several scales. We tested the potential of nonlinear dynamics analyses to identify loss and recovery of consciousness at clinically relevant timepoints. Patients undergoing propofol general anesthesia for various surgical procedures were identified as having changes in states of consciousness by the loss and recovery of response to verbal stimuli after induction and upon cessation of anesthesia, respectively. Nonlinear dynamics analyses showed more significant differences between consciousness states than most spectral measures. Thus, complexity measures could provide a means for reliably capturing depth of consciousness based on subtle EEG changes at the beginning and end of anesthesia administration.

Unmet dementia-related care needs are highly prevalent, and are detrimental to the care dyad’s health and well-being, safety, and ability to age in place. The goal of this study was to develop an ecologically-valid needs assessment and integrate it with aspects of the SHARE intervention to inform values-based care planning. Using digital behavioral data collected via an actigraphy watch and multimodal sensors installed in the homes of 76 older adult couples with and without dementia, we created a prototype of the objective measures informing READyR: time spent together or separate as a dyad, exits from the home, sleep habits, physical activity, daily weight, driving habits, and medication taking behavior. These digital behavioral data were then mapped onto care values (e.g. safety, avoiding burden & autonomy) to create a values-based needs assessment protocol that is tailored to the individual care dyad. Discussion will focus on future testing and applications of READyR.

Agitation is a challenging dementia-related behavioral symptom and disconcertingly leads to an undesirable increase in pharmaceutical intervention. Agitation is primarily assessed subjectively by time-impoverished caregivers or clinicians, who may not be able to provide comprehensive or sensitive enough reporting to detect early signs of agitation or identify its precipitants. The purpose of the MODERATE (Monitoring Dementia Related Agitation Using Technology Evaluation) study is to characterize dementia-related agitation and its potential catalysts using wearables and sensors installed in participants’ private living quarters within memory care (skilled nursing) facilities. Preliminary data from seven episodes of agitation–reported by nursing staff and treated by pharmaceutical intervention–were captured and characterized with an integrated network consisting of research-grade actigraphy, passive infrared presence sensors, door contact sensors, ambient environmental sensor boards, and bed sensor mats. Behaviors (e.g. pacing, space transitions) during 8-hour nursing shifts that included episodes of agitation were compared with 8-hour nursing shifts without any episodes of agitation. The environmental conditions (temperature, lighting, humidity, and decibel level) during agitated periods and non-agitated periods were also compared. There was a statistically significant difference between agitated periods and non-agitated periods for the number of space transitions in the living room (p

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

We present two methods for generating novel hyper-crossing tool paths for use with CNC and robotic sub-aperture polishing techniques. One generation method utilizes an optimization based on a Voronoi diagram. The second method is seeded by a unicursal pseudo-random pattern to generate a hyper-crossing tool path which features many self-crossings over the entire part surface. Each instance of generation for a given surface for either algorithm will result in a new hyper-crossing pattern. Tool paths can be generated for any surface area including those with interior holes. We also present results of an experiment using a hyper-crossing tool path to remove diamond turning marks.

Aluminum (pure or alloy) mirrors attract increasing interest, having Young’s Modulus and density similar to glasses. Advantage of high diffusivity offsets disadvantage of high thermal expansion coefficient and means that the mirror reaches thermal equilibrium rapidly. High ductility supports extreme light-weighting and complex machining, including fluid-cooling channels in high-energy applications, and integral interface components. Aluminum mirrors are also tolerant to vibrations and shock loads. The material is amenable to single point diamond turning (SPDT) and does not require optical coating. However, SPDT tends to produce mid-spatial frequency artefacts, which are difficult to remove, especially for aspheres and free-forms. These introduce diffraction effects and compromise stray light performance. In our previous research, we have demonstrated the potential of industrial robots to automate manual interventions with CNC polishing machines, and to provide surface-processing capabilities in their own right. We have also presented research concerning the mismatch between rigid and semi-rigid tools (including non-Newtonian tools), and aspheric surfaces. In this paper, we report on polishing of spherical and aspheric aluminum mirrors using an industrial robot. This includes tool-design, tool-path generation, texture control and removal of the mid-spatial frequency artefacts. We have investigated removal-rates and textures achieved, using different specialized slurries, polishing pads and special tool-paths. An effective process has been established, achieving Sa of 5nm on a 400mm square witness sample and a 490mm elliptical off-axis parabolic mirror.

In the context of Industrie 4.0, we have previously described the roles of robots in optical processing, and their complementarity with classical CNC machines, providing both processing and automation functions. After having demonstrated robotic moving of parts between a CNC polisher and metrology station, and auto-fringe-acquisition, we have moved on to automate the wash-down operation. This is part of a wider strategy we describe in this paper, leading towards automating the decision-making operations required before and throughout an optical manufacturing cycle.

This paper analyzes the impact of tool mass acceleration (TMA) values onto footprint shape stability in computer controlled polishing, presenting the second derivative footprint recording (SECondo) method. First experimental evidence is presented, demonstrating that for bonnet polishing, acceleration of tool mass significantly alters the pressure distribution within the footprint and consequently affects its cross section. In addition, we present experimental data indicating that it is possible to compensate for TMA effects by adjusting the inclination angle of precession during CCP accordingly.

Mid-spatial frequency (MSF) errors challenge freeform manufacture, not in the least due to tool-misfit. This can compromise the performance of functional surfaces and is difficult to remove by post-processing. Our previously reported work on an effective process-chain for aluminum polishing demonstrated the ability to remove MSFs by hard-tool grolishing. In this paper, we describe MSF removal on an aluminum mirror, deformed to a saddle-like freeform shape, using power spectral density (PSD) as a diagnostic. CNC Precessions bonnet polishing was optimized to minimize output MSFs, then a non-Newtonian (n-N) tool was used to attenuate the residual MSFs that were present. Our approach was distinct from the approach pioneered by University of Arizona, in that we adopted small-tool polishing on the saddle-like part, with removal rate restored by rotating the n-N tool. In order to define the optimum window of rotation speeds, the dynamic behavior of the n-N material was explored by modelling and experiments. The tool was deployed on an industrial robot, and we describe a novel 'hyper-crossing' tool-path with wide sweeping paths, which is the logical opposite of the unicursal zero-crossing paths we have previously reported. This new path has proved ideally suited to robots, given their high velocity/acceleration capabilities. Detailed results are presented from the PSD viewpoint.

This paper investigates the differentiation between machine’s static and dynamic footprint (FP). The results have shown progressing footprint variation related to tool’s tilt angle. Continuous tilt angle compensation has been applied to offset this effect.

Assessment of subjective and objective effects of caregiving most commonly occurs using questionnaires or diaries, which can be subject to inaccuracies in self-reporting. Given the inherent difficulty of accurately reporting activities related to caregiving, tools that objectively measure aspects of caregiving are needed. Here we report findings from a natural history clinical trial enrolling 30 participants with MCI or Alzheimer’s disease living with a caregiver. A pervasive sensing and computing system is deployed in each couple’s home for one year, continuously providing data on measures related to caregiver engagement. Caregivers are accepting of the home system (no dropouts), which provides information on time spent together and sleep quality in the caregiver. This system provides an unobtrusive and longitudinal method to study activity and effort related to caring for patients with AD in the community and could be used to show quantifiable improvement in caregiver-related outcomes in trials testing caregiver-directed interventions.