prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What types of sensors are now capable of estimating physical activity levels and physiological outcomes of older adults?

Read the following text and answer briefly.

Context 1: \section{Introduction}
Cognitive deficit of older adults is one of the biggest global public health challenges in elderly care  Approximately 5 2 million people of 65 and older are suffered with any form of cognitive impairments in United States in 2012 \cite{stat12}  Dementia is one of the major causes of the cognitive impairments which is more acute among 85 and older population (50\%) \cite{stat12}  However, the costs (financial and time) of health care and long-term care for individuals with Alzheimer's (special form of dementia) or other dementias are substantial  For example, during 2016, about 15 9 million family and friends in United States provided 18 2 billion hours of unpaid assistance to those with cognitive impairments which is a contribution to the nation valued at \$230 1 billion  One the other hand, total payments for all individuals with all form of cognitive impairments are estimated at \$259 billion  Total annual payments for health care, long-term care and hospice care for people with Alzheimer's or other dementias are projected to increase from \$259 billion in 2017 to more than \$1 1 trillion in 2050  Among the above costs, a significant amount are relevant to clinical and diagnostic tests \cite{stat17}  Although clinical and diagnostic tests have become more precise in identifying dementia, studies have shown that there is a high degree of underrecognition especially in early detection  However, there are many advantages to obtaining an early and accurate diagnosis when cognitive symptoms are first noticed as the root cause findings of impairment always lessen the progress of impairment status and sometimes symptoms can be reversible and cured 

With the proliferation of emerging ubiquitous computing technologies, many mobile and wearable devices have been available to capture continuous functional and physiological behavior of older adults  Wearable sensors are now capable of estimating number of steps being taken, physical activity levels, sleep patterns and physiological outcomes (heart rate, skin conductance) of older adults \cite{sano15}  Ambient sensors also help capture the movement patterns of objects and humans for activity and behavior recognition \cite{dawadi14,dawadi15}  Researchers also proved the existence of correlations between cognitive impairment and everyday task performance \cite{dawadi14, akl15,alam16} as well as physiological symptoms \cite{alam16,sano15}  Although current studies showed some successes in IoT-assisted cognitive health assessment in different domains individually, there are several existing challenges in developing and validating a fully automated multi-modal assessment model 

\begin{enumerate}
\item \emph{Real-time IoT System}: A real-time IoT system must include a continuous and fault tolerant data streaming capability among central hub, wearable sensors and ambient sensors regardless of network communication protocol (WiFi, Ethernet, Bluetooth etc ) which are not available in existing researches 
\item \emph{Multi-modal Context Fusion}: Though several offline clinically validated cognitive health assessment tools exist \cite{wai03, starling99, krapp07, yesavage82, zung71}, there is no universally accepted method for IoT-assisted automatic cognitive health assessment in smart home environment that can fuse multi-modal sensor contexts altogether  For example, some researchers showed ambient sensors based Activities of Daily Livigin (ADLs) sequence pattern can signify the cognitive health status of older adults \cite{akl15, dawadi15}  Researchers also showed wearable Electrodermal Activity pattern analysis may carry the significance of cognitive status \cite{sano15}  However, for validation of IoT based cognitive health assessment, self-reported surveys, clinical diagnosis and observation based tools are used individually by prior researchers \cite{akl15, dawadi15, sano15, alam16} 
\end{enumerate}

Regarding aforementioned challenges for the automation of cognitive health assessment, \emph{AutoCogniSys} considers (i) reproducibility of our model in any smart home system consists of ambient motion sensors, wearable accelerometer (ACC) sensors, wearable Electrodermal Activity (EDA) and Photoplethysmography (PPG) sensors individually or combined streams; (ii) context awareness based on ambient motion sensors and wearable ACC sensors in any types of activities such as hand gestural, postural and complex ADLs; and (iii) high accuracy, i e , a recall rate of over 90\% with less than 5\% false positive rate  More specifically, \emph{AutoCogniSys} extends our existing work \cite{alam16} in three dimensions,

\emph{(1) True Automation:} We first investigate the correlations of cognitive impairment with human activities and stress where we manually labeled activities, extract the corresponding physiological sensor (EDA and PPG) features of each activity, and use statistical method to find correlations  Then, we propose automatic complex activity recognition based on a Hierarchical Dynamic Bayesian Network (HDBN) model, fine-grained extraction of physiological sensor features and finally machine learning classification of cognitive impairment 

\emph{(2) Noises Elimination:} We define different types of noises on ACC, EDA and PPG sensors, propose extensive signal processing techniques to remove noises and show significant improvement can be achieved in cognitive impairment classification 

\emph{(3) Implementation and Evaluation:} Finally, we design and implement IoT system and analytic methods and minimize the human involvement to automate our proposed cognitive health assessment approach by considering effective smart home sensor customization and deployment, data collection, screening, cleaning and filtering, feature computation, normalization and classification, and activity  model training 

\textbf{Research Questions:} \emph{AutoCogniSys} consequently tackles the following key research questions 

$\bullet$ Can we detect simultaneously the periodic rhythms of  both hand gestures and postural activities from wrist-worn ACC sensor signal for diverse population (population with same activity but diverse ways such as walking with walker, stretcher or normally)? If so, how can we incorporate the hand gesture, posture and ambient sensor data streams to help improve the ADLs recognition models?

$\bullet$ How can we exploit and relate the micro-activity features into noise free physiological sensor signals processing to automate cognitive health assessment process? What are the critical roles of clinical survey and technology guided assessment methodologies and their inter-relationships for automating the different intermediate steps of cognitive health assessment process?

To tackle these, we make the following \textbf{key contributions}:

$\bullet$ We employ an extensive signal deconvolution technique that in conjunction with machine learning technique helps facilitate a wrist-worn ACC-based multi-label (hand gestural and postural) activity recognition for diverse population  We then leverage multi-label context sets with ambient and object sensor signals for complex activity recognition based on HDBN model 

$\bullet$ We propose a novel collaborative filter for EDA signal processing by postulating signal as a mixture of three components: \emph{tonic phase, phasic phase} and \emph{motion artifacts}, and employ convex optimization technique for filtering out the motion artifacts  We also propose a novel PPG signal processing technique to filter out the inherent motion artifacts and noises using improved Periodic Moving Average Filtering (PMAF) technique 

$\bullet$  We design and prototype an IoT system consisting of multiple devices (wearable wrist band, IP camera, object and ambient sensors) connected with central hub via WiFi, Ethernet and Bluetooth communication protocols  We collected data from 22 older adults living in a continuing care retirement community center in a very natural setting (IRB \#HP-00064387) 

$\bullet$ Finally, we employ statistical and machine learning techniques to jointly correlate the activity performance metrics and stress (EDA and PPG) features that helps achieve max  93\% of cognitive impairment status detection accuracy  We evaluate \emph{AutoCogniSys} on 5 clinically validated offline assessment tools as ground truth 
\section{Related Works}
\emph{AutoCogniSys} builds on previous works on wearable devices based low-level (postural and hand gestural) activity recognition and their integration with ambient sensors to recognize complex ADLs, the underlying signal processing and applications on cognitive health assessment automation 
\subsection{Wearable Sensor Signal Processing}
Wearable sensors can be two types: physical and physiological  Physical sensors (accelerometer, gyroscope etc ) signal values change over the movements of the sensor devices  Physiological sensors change over physiological condition of body such as EDA changes over stress and PPG changes over heart rate  However, physical movements also impose noises on physiological sensor signals which is called \emph{motion artifacts} 
\subsubsection{Physiological Signal Processing}
A continuous and descrete decomposition of EDA, and time and frequency domain analytics of PPG signal have been investigated before to extract relevant physiological features which were contaminated with noises and motion artifacts \cite{alam16}  \cite{setz10} denoised and classified EDA from cognitive load and stress with accuracy higher than 80\%  Though motion artifacts removal techniques such as exponential smoothing \cite{hern11} and low-pass filters \cite{poh10, hernandez14} provide significant improvement in filtering EDA signals, wavelet transforms offer more sophisticated refinement for any kind of physiological sensors such as electroencephalogram \cite{krish06, zikov02}, electrocardiogram \cite{erc06,alfa08}, and PPG \cite{lee03}  \cite{chen15} proposed a stationary wavelet transform (SWT) based motion artifacts removal technique  `cvxEDA' proposed  a convex optimization technique considering EDA as a mixture of white gaussian noise, tonic and phasic components where white gaussian noise includes motion artifacts and external noises \cite{greco16}  \emph{AutoCogniSys} intelligently combines SWT and `cvxEDA' together to remove noises and motion artifacts from EDA signal  On the other hand, it is more difficult to remove motion artifacts from PPG signal due to its periodicity of nature \cite{wang13}  Researchers proposed different methods such as frequency analytics \cite{garde13,wang13}, statistical analytics \cite{peng14} and digital filter \cite{lee10} to reduce noises and motion artifacts from PPG  \emph{AutoCogniSys} used Periodic Moving Average Filter (PMAF) in this regard \cite{lee07} 
\subsubsection{Physical Sensor Signal Processing}
ACC based hand gesture recognition has been explored by several researchers in past such as discrete hidden markov model \cite{liu10}, artificial neural network \cite{arce11}, weighted naive bayes and dynamic time warping \cite{mace13}  Akl et  al  proposed 18 gesture dictionary based Support Vector Machine (SVM) classifier \cite{akl11}  Wrist-worn ACC based postural activity recognition approach has been proposed using Decision Tree, Random Forest, Support Vector Machines, K-Nearest Neighbors, Naive Bayes and deep neural networks \cite{gj14, wang16}, the accuracy stagnates at 85\% using SVM method \cite{martin16}  However, neither of past works proposed any technique that can provide single body worn ACC sensor-based multiple body contexts recognition nor works efficiently for diverse posture say walking normally, with walker, with double walker or wheel chair  Our proposed 8-hand gesture recognition technique assisted sparse-deconvolution method improves classification performances on both normal and diverse postures  However, we incorporated hand gestures and postures in conjunction with ambient sensors into single-inhabitant HDBN model \cite{alam16b} that provides significant improvement in complex activity recognition 
\subsection{Cognitive Health Assessment}
Smart home environment has been used for providing automated health monitoring and assessment in the ageing population before \cite{dawadi14, gong15, akl15, dawadi15}  `SmartFABER' proposed a non-intrusive sensor network based continuous smart home environmental sensor data acquisition and a novel hybrid statistical and knowledge-based technique to analyz the data to estimate behavioral anomalies for early detection of mild-cognitively impairment \cite{riboni16}  \cite{skubic15} presented an example of unobtrusive, continuous monitoring system for the purpose of assessing early health changes to alert caregivers about the potential signs of health hazards  Though, prior researches proposed a sequence of ambient motion sensor streams as complex activity components in activity based health assessment \cite{dawadi14, gong15, akl15, dawadi15}, we consider inclusion of an wearable wrist-band with in-built ACC sensor to detect hand gesture and posture, augmenting with the ambient sensor readings to help recognize complex activities as well as cognitive health assessment of older adults  Additionally, we propose intelligent use of physiological features of skin through different physiological sensor signals (EDA, PPG) processing in daily activity tasks and incorporate context-awareness for automation of cognitive health assessment that have not been explored before 
\begin{figure}[!htb]
\begin{center}
   \epsfig{file=flowchart pdf,height=1 6in, width=3 5in}
\caption{Overall flow of \emph{AutoCogniSys} pipeline }
   \label{fig:overview}
\end{center}
\end{figure}
\section{Overall Architecture}
We first investigate existing IoT-based cognitive health care frameworks that covers every aspects of wearable (physical, physiological) and ambient (passive infrared and object) sensor signals computing  \emph{AutoCogniSys} is comprised of three component modules: (i)~sensing, (ii)~processing, and (iii)~analysis  The `sensing' module consists of clinical assessment tools (surveys, observation and clinical backgrounds) and sensing signals (ambient and wearable sensors)  `Sensor processing' module is comprised of three sub-modules: a)~clinical assessment feature extraction from assessment tools; b)~ambient sensor feature extraction; and c)~wearable sensor processing (noise removal, segmentation, feature extraction, classification etc )  `Analysis' module is comprised of machine learning and statistical analytics-based score prediction of cognitive impairment  Automation of each module's functionality and inter-intra modular transactions without human interference can be called {\it true automation} of cognitive health assessment  Fig ~\ref{fig:overview} shows the overall flow of \emph{AutoCogniSys} which is discussed in details in the following sections 
\subsection{Demographic Ground Truth Data Collection}
Currently in standard clinical practice and research, the most accurate evaluations of cognitive health assessment are one-to-one observation and supervision tasks/questionnaires for monitoring an individual's functional abilities and behavior \cite{resnick15}  In the first stage of this pilot study, we have investigated current literatures and carefully chosen the clinically proven functional and behavioral health assessment survey tools \cite{resnick15}  On the other hand, to cross check with the survey based evaluations, we have also chosen clinically justified observation based behavioral assessment methods  First, following the resident consent, our clinical research evaluator collects demographic and descriptive data (age, gender, race, ethnicity, marital status, education and medical commodities)  She has performed two types of clinical assessments: (1) \emph{Observation based} where the resident's cognition is assessed using the Saint Louis University Mental Status (SLUMS) scale \cite{wai03}  (2) \emph{Survey based} where five widely used and clinically well validated surveys are taken into account: (a) \emph{Yale Physical Activity Survey} \cite{starling99}; (b) \emph{Lawton Instrumental Activities of Daily Living}; (c) \emph{Barthel Index of Activities of Daily Living} \cite{krapp07}; (d) \emph{Geriatric Depression Rating scale} \cite{yesavage82}; and (e) \emph{Zung Self-Rating Anxiety scale} \cite{zung71} 
\subsection{Smart Environment Creation}
For an ideal IoT-based system, instrumenting and deploying it at each participant's natural living environment warrants for assembling a flexible set of hardware and software interfaces to ease the system configuration, setup, and network discovery processes  The sensor system placed in the residences of volunteers needs to meet several specific physiological signals and activity monitoring needs  However, we must confirm that the devices are reliable with potential for re-deployment as well as appear unintimidating to the participants  Inspired by the above requirements, we developed a real testbed IoT system, {\it SenseBox}, by customizing Cloud Engine PogoPlug Mobile base station firmware to integrate with WiFi (connect ambient and object sensors) and Bluetooth (connect wristband) protocol  The smart home components are as follows: (i) PogoPlug base server with a continuous power supply, (ii) 3 binary Passive Infrared sensors in three different rooms (kitchen, livingroom and bedroom) to capture room level occupancy, (iii) 7 binary object sensors attached with closet door, entry door, telephone, broom, laundry basket, trash can and trash box, (iv) three IP cameras in the appropriate positions to collect the ground truth data and (v) an Empatica E4 \cite{empatica} wrist-band (integrated sensors: PPG at 64 Hz, EDA at 4 Hz, Body temperature at 1 Hz and a triaxial ACC at 32 Hz) on the participant's dominating hand 
\section{Activity Recognition}
We aim to detect single wrist-worn ACC sensor based hand gesture and postural activities and insert these into an HDBN graphical model in conjunction with ambient and object sensor values for complex activity recognition  We consider the recognition problem asan activity tupple of $\langle gesture,posture,ambient,object \rangle$  Though, Alam et  al  provides significant performance improvement for single wrist-worn ACC sensor aided 18-hand gesture based postural activity recognition in lab environment \cite{alam17}, it faces some practical challenges in real-time smart environment with older adults due to the diversity of their postures  For example, some older adults use walker, double walking sticks or wheel chair for walking in which cases collecting 18 hand gestures and corresponding postural activities for training requires endless efforts and carefulness  To reduce the complexity of ground truth labeling and later state space explosion for graphical model (HDBN), we propose to use rotational normalization method that can merge some hand-gestures subject to directional differences and forms an 8-hand gesture model  However, our proposed Feature Weight Naive Bayes (FWNB) classifier adds significant improvement on Alam et  al  proposed sparse-deconvolution method as well as recognition in diverse postural environment 
\begin{figure}[!htb]
\begin{center}
   \epsfig{file=hand_gestures pdf,height=0 5in, width=3in}
   \vspace{- 2in}
\caption{8 hand gesture dictionary with direction}
   \label{fig:hand_gestures}
   \vspace{- 2in}
\end{center}
\end{figure}
\subsection{Hand Gesture Recognition}
\label{sec:hand_gesture}
\emph{AutoCogniSys} proposes an 8-gesture dictionary (as shown in Fig  \ref{fig:hand_gestures}) and a Feature Weighted Naive Bayesian (FWNB) framework for building, modeling and recognizing hand gestures  The method comprises of the following steps: (i) \emph{Preprocessing:} wrist-worn ACC sensor provided 3-axis data are passed through 0 4Hz low-pass filter to remove the data drift  (ii) \emph{Rotation normalization:} Normalizing the rotation of hand gestures provides greater accuracy and allows for more realistic, orientation-independent motion  At first, we find the best fit plane of the acceleration vectors thus if the motion lies in a single plane, then the acceleration vectors of a closed shape should on average lie in that main plane  Then, we take all acceleration segments between points of inflection to form one single vector called reference vector that provides us the general direction of user's motion  After that, each vector is normalized relative to the reference vector  This normalization helps remove a lot of hand gestures from prior considered 18 hand gestures resulting a reduced dictionary of 8 gestures  (iii) \emph{Feature Weighted Naive Bayesian model:} Naive Bayes classifier is light-weight and efficient technique for hand gesture recognition  We extract 12 ACC features \cite{alam17} and calculate weight for each feature type based on the similarity of feature measures of the trained gestures (0$<$weight$<$1)  While recognizing gestures, the proximity of each feature measure to the average trained feature measure of each gesture type is calculated by a normal distribution  Then, the proximity value is multiplied by the feature weight that was calculated in the training phase   All of these multiplied values are added together and the system predicts the gesture type with the greatest value as the user gesture  In the learning data points, there should be static postural activities (such as sitting, lying etc ) to avoid unexpected noises over wrist-worn ACC sensors  In the final hand gesture dictionary, we save the reference vector as our signal dictionary 
\subsection{Postural Activity Recognition}
In normal lab environment, wrist-worn ACC sensor signal is a mixture (convolution) of actual hand gesture and postural activity relevant signals \cite{alam17}  \emph{AutoCogniSys} improves the idea by reducing the number of hand gestures and postural activities to 8 (as shown in Fig \ref{fig:hand_gestures}) using rotation normalization and 4 (walking, sitting, standing and lying)  Then, we use sparse-deconvolution method (with 31\% signal reconstruction error) to get Approximately Sparse Factor  The summary of the entire process is stated bellow:

{\it Building Deconvolution Method:} We first consider the wrist-worn ACC sensor signals (3-axis values) as a convolution of hand gesture and postural activity effects and build a deconvolution framework  The deconvolution framework takes a known signal (hand gesture effects) and a equalizer parameter ($\lambda$) as input and provides an Approximately Sparse Factor signal (postural activity effects) as output  For 3-axis ACC signals, we need to learn associated 3 equalizer parameters for each hand gesture  Moreover, each equalizer parameter is involved with 4 postural activities that results a total 96 ($8\times 3\times 4$) equalizer parameters to learn  

{\it Learning Classification Model:} We use the Approximately Sparse Factor signal to extract 12 statistical features and SVM with sequential machine optimization (SMO) \cite{cao06} for postural activity recognition 

{\it Prediction Model:} After recognizing the hand gestures following the method explained in Sec ~\ref{sec:hand_gesture}, we take the corresponding reference vector as known signal and extract the Approximately Sparse Factor signals incorporating corresponding 3 equalizer parameters ($\lambda$) for the sparse-deconvolution method  Then, we apply feature extraction and prior learned SMO based SVM classifier \cite{cao06} to classify final postural activity  Fig ~\ref{fig:deconvolution} illustrates a single axis example of the deconvolution 

\begin{figure}[!htb]
\begin{center}

   \epsfig{file=deconvolution pdf,height=1 6in, width=3in}
   \vspace{- 15in}
\caption{Sample deconvolution example of X-axis  The raw x-axis of accelerometer signal, reference vector of the sample gesture and the extracted corresponding ASF signal of walking }
   \label{fig:deconvolution}
\end{center}
\vspace{- 15in}
\end{figure}

\subsection{Complex Activity Recognition}
We build a HDBN based complex activity recognition framework for single inhabitant scenario smart home environment \cite{alam16b} taking the advantage of detected hand gestural and postural activities along with the ambient and object sensor streams  At first, we obtain instant hand gestural and postural activities from our above proposed models, and additionally motion sensor and object sensor readings from our IoT-system for every time instant generating a 4-hierarchy of HDBN model  Considering the context set $\langle gestural, postural, ambient,object\rangle$ as a hierarchical activity structure (extending two 2-hierarchical HDBN \cite{alam16b}), we build complex activity recognition model for single inhabitant scenario  Finally, we infer the most-likely sequence of complex activities (and their time boundaries), utilizing the well-known Expectation Maximization (EM) algorithm \cite{dempster77} for training and the Viterbi algorithm \cite{forney73} for run-time inference 
\section{Automatic Activity Features Estimation}
The effects of cognitive ability on daily activity performance have been studied before \cite{dawadi14,akl15}  They experimentally and clinically validated that cognitive impairment highly reduces the daily activity performances and this activity performance can be computed as an indicator of cognitive ability status of older adults  The standard activity features refer to completeness of task (TC), sequential task ability (SEQ), interruption avoidance capabilities (INT) etc  In current behavioral science literature, the above activity features carry specific definition based on the sub-tasks involved with a complex activity \cite{dawadi14,akl15}  Completeness of task refers to how many sub-tasks are missed by the participants  Sequential task ability refers to how many sequences of sub-tasks are missed referring the gerontologist defined standard sequences of the sub-task for the particular complex activity  Interruption avoidance capability refers to how many times the participants stop or interleave while doing any sub-task  The final goal of activity features estimation is to provide overall task score  The task score is proportional to the functional ability of participants in performance daily activities  Our behavioral scientist team, comprises with Nursing professor, gerontologist and retirement community caregivers, carefully discus, optimize and choose 87 sub-tasks in total for 13 complex activities 

Each of the sub-task comprises with sequential occurrences of hand gesture and postural activities  However, no researchers ever considered hand gesture for activity features estimation due to complexity of multi-modal wearable and ambient sensors synchronization and multi-label activity classification \cite{dawadi14,akl15}  \emph{AutoCogniSys} exploited single wrist-worn sensor based hand gesture and postural activity recognition, and proposed an activity features (TC, SEQ and INT) estimation method including these two parameters in conjunction with object and ambient sensor features that provide significant improvement of cognitive health assessment of older adults 
\subsection{Machine Learning Based Complex Activity Features Estimation}
In current cognitive health assessment literature, complex activity features can be defined as $\langle TC,SEQ,INT,TS\rangle$  We used supervised method to estimate TC, SEQ and INT, and unsupervised method to estimate TS  We first, formulate the automated scoring as a supervised  machine learning problem in which machine learning algorithms learn a function that maps $\langle${\it hand gesture, posture, object, ambient sensor}$\rangle$ feature set to the direct observation scores  We use bagging ensemble method to learn the mapping function and SMO based SVM \cite{cao06} as base classifier  The learner averages by boostrapping individual numeric predictions to combine the base classifier predictions and generates an output for each data point that corresponds to the highest-probability label  We train three classifiers considering observation as ground truth for TC, SEQ and INT scores and test on the testing dataset  We derive unsupervised scores using dimensionality reduction technique for each feature set  First, we take all features of each activity, apply optimal discriminant analysis technique as a dimensionality reduction process \cite{zhang09} and reduce the feature sets into single dimensional value which represents the automated task completeness scores of the particular user activity  A min-max normalization is applied that provides us a uniform range of the variables using $
z_i=\frac{x_i-min(x)}{max(x)-min(x)}$ equation where $x=\{x1,\ldots,x_n\}$ and $z_i$ is $i^{th}$ normalized data  The final single dimensional score represents machine learning based TS score 
\section{Physiological Sensor Signals Processing}
The autonomic nervous system (ANS) restrains the body's physiological activities including the heart rate, skin gland secretion, blood pressure, and respiration  The ANS is divided into sympathetic (SNS) and parasympathetic (PNS) branches  While SNS actuates the body's resources for action under arousal conditions, PNS attenuates the body to help regain the steady state  Mental arousal (say stress, anxiety etc ) activates the sweat gland causing the increment and reduction of  Skin Conductance on SNS and PNS physiological conditions respectively  However, Instant Heart Rate also has similar effect on SNS and PNS physiological condtions i e , a higher value of heart rate is the effect of SNS and lower value is the outcome of PNS  EDA and PPG sensors are widely used to estimate the instant value of skin conductance and heart rate respectively \cite{alam16} 
\subsection{EDA Sensor Signal Processing}
EDA is the property of the human body that causes continuous variation in the electrical characteristics of the skin which varies with the state of sweat glands in the skin  There are three types of arousal: \emph{cognitive, affective and physical}  \emph{Cognitive} arousal occurs when a person tries to solve any problem using her cognitive ability  \emph{Affective} arousal occurs when a person is worried, frightened or angry either doing daily activities or in resting position  On the other hand, \emph{physical} arousal is related to the brain command to move bodily parts which is imposed on the total arousal as an artifact, called \emph{motion artifact}  However, there are always some noises due to the weather conditions (temperature, humidity etc ) and device motion  This \emph{motion artifact} can be the prime cause of signal contamination of physiological outcomes while performing daily activities which must be removed  \emph{AutoCogniSys} proposes an EDA sensor signal processing method consists of three steps: (i) noise and motion artifacts removal, (ii) separation of tonic component and phasic component (explained later) from contamination free EDA signal and (iii) feature extraction on the response window 
\subsubsection{Motion Artifacts Removal}
There are many types of motion artifacts but the unsual steep rise is the mostly occured ones associated with EDA signal while performing daily activities \cite{edel67}  We use well-known steep rising noises reduction technique, SWT \cite{chen15}  We first consider EDA signal as a mixture of a slow variant tonic and fast variant phasic component, i e , SWT coefficient is modeled as a mixture of two Gaussian components, phasic (close to zero valued signal) and tonic (high rising signal)  After expanding EDA signal into multiple levels of scaling and wavelet coefficients, we choose adaptively a threshold limit at each level based on the statistical estimation of the wavelet coefficients' distribution, and employ that on the wavelet coefficients of all levels  Finally, an inverse wavelet transform technique is applied to the thresholded wavelet coefficients to obtain the artifacts free EDA signal  Fig~ \ref{fig:eda_artifact_removal} shows a sample of raw and motion artifacts free EDA signal 

\begin{figure}[!htb]
\begin{center}
\vspace{- 1in}
   \epsfig{file=eda_signal_artifact pdf,height=1 6in, width=3 5in}
\caption{Dashed line represents noisy EDA signal and solid red line represents \emph{AutoCogniSys} proposed motion artifact free EDA signal}
   \label{fig:eda_artifact_removal}
\end{center}
\end{figure}
\subsubsection{Convex Optimization Technique to EDA Deconvolution}
After the motion artifact removal, we consider EDA as the sum of three components for $N$ sample: a slow tonic driver ($t$), fast (compact, bursty) non-negative sparse phasic driver ($r$) and a reminder error term ($\epsilon_r$) 
\begin{equation}
\label{eq:eda_signal}
y = t + r + \epsilon_r
\end{equation}
This additive error $\epsilon_r$ is a White Gaussian Noise  The central problem associated with the deconvolution method is to get tonic $t$ component from the above equation  \cite{greco16} showed that EDA signal deconvolution (separation of tonic, phasic and noise terms from EDA signal) is a quadratic optimization problem and defined tonic component as follows:
\begin{equation}
\label{eq:tonic}
t = Bl + Cd,
\end{equation}
where $B$ is a tall matrix whose columns are cubic $B$-spline basis functions, $l$ is the vector of spline coefficients, $C$ is a $N\times 2$ matrix, $d$ is a $2\times 1$ vector with the offset and slope coefficients for the linear trend  The above equation is subject to the following optimization problem,
\begin{eqnarray}
minimize \frac{1}{2} {||Mq + Bl + Cd- y||}^2_2 +\alpha {||Aq||}_1 + \frac{\lambda}{2} {||l||}^2_2\\
subject\;to\; Aq \geq 0\nonumber
\end{eqnarray}
where $M$ and $A$ are tridiagonal matrices and $q$ is an auxiliary variable  After solving the above equation, we can get the optimal values for $\{q,l,d\}$ that can be used to obtain tonic component from the equation~\ref{eq:tonic}  The reminder of the equation~\ref{eq:eda_signal} ($r+\epsilon_r$) is considered as a mixture of White Gaussian Noise ($\epsilon_r$) and a fast variant phasic component ($r$)  We employ butterworth low-pass filter (5Hz) and hanning smoothing with window size 4 (optimal) to remove $\epsilon_r$ from phasic component ($r$) 
\subsection{PPG Signal Processing}
PPG is used mainly for measuring the oxygen saturation in the blood and blood volume changes in skin  An ideal PPG signal processing must contain the following steps: noise and motion artifacts removal, heart rate detection, heart rate variability estimation and feature extraction 
\subsubsection{PPG Signal Noise and Motion Artifacts Removal}
Similar to EDA signal, PPG signal is also contaminated with motion artifacts and noises  However, unlike EDA signal, PPG produce quasiperiodicity in a time series spectrum \cite{mete30}  We use Periodic Moving Average Filter (PMAF) to remove motion artifacts and noises \cite{lee07}  We first segment the PPG signal on periodic boundaries and then average the $m^{th}$ samples of each period  After filtering the input PPG signal with a 5-Hz $8^{th}$-order Butterworth low-pass filter, we estimate the maximum and minimum value of each period  The mean of each period are obtained from the maximum and minimum values applying the zero crossing method  These points of the means help determine the boundaries of each period  Then, interpolation or decimation is performed to ensure that each period had the same number of samples \cite{lee07}  
\subsubsection{Heart Rate and Heart Rate Variability Estimation}
We first apply PMAF on PPG signal to remove noises and motion artifacts, refine PPG by smoothing the signal using 1-dimensional Gaussian Filter and Convolution, calculate first derivative of the convoluted signal and finally find the differences between two consecutive peak values which is called HRV \cite{sel08}  The occurrences of total peak values (R-peak or beat) in each minute is called Heart Rate (HR) with an unit of Beat Per Minute  The signal value property of HRV and HR are inversely proportional which means the mental arousal that increases HR should decrease HRV in the time segment window  Fig~\ref{fig:ppg_artifact_removal} shows a sample of the noisy and filtered PPG signal and their corresponding Instant Heart Rate 
\begin{figure}[!htb]
\vspace{- 1in}
\begin{center}
   \epsfig{file=ppg_artifact_removal pdf,height=1 4in, width=3 5in}
   \vspace{- 15in}
\caption{Top figure illustrates the noisy signal (dotted line) and filtered signal from PPG sensor based on our filtering method  Bottom figure illustrates instant heart rate calculated from noisy signal (dotted line) and filtered signal}
   \label{fig:ppg_artifact_removal}
\end{center}
\vspace{- 15in}
\end{figure}
\subsection{Physiological Sensor Signal Feature Extraction}
Using the above mentioned methods, we removed the noises and motion artifacts from EDA and PPG signals and generated two time series signal from EDA (tonic and phasic components) and one time series signal from PPG (HRV)  Then, we segment each of the time series signal based on our prior detected complex activities such that each response window starts and ends with the starting and ending points of each complex activity  We extract 7 statistical time-series features for EDA (as shown in Table~\ref{tab:eda_features}) and 8 features for HRV (Table~\ref{tab:hrv_features}) within the response window) 

\begin{table}[!t]
\begin{center}

\renewcommand{\arraystretch}{1}
\caption{EDA Features Within The Response Window}
\begin{scriptsize}


\label{tab:eda_features}
\begin{tabular}{|c|l|}
\hline
\bfseries Features& \bfseries Description\\
\hline
nSCR & Number of SCRs within response window (wrw)\\
\hline
Latency & Response latency of first significant SCR wrw\\
\hline
AmpSum & Sum of SCR-amplitudes of significant SCRs wrw\\
\hline
SCR & Average phasic driver wrw\\
\hline
ISCR & Area (i e  time integral) of phasic driver wrw\\
\hline
PhasicMax & Maximum value of phasic activity wrw\\
\hline
Tonic & Mean tonic activity wrw\\
\hline
\end{tabular}
\end{scriptsize}
\end{center}
\end{table}



\begin{table}[!t]
  \begin{center}
\renewcommand{\arraystretch}{1}
\vspace{- 3in}
\caption{Heart Rate Variability features}
\label{tab:hrv_features}
\begin{scriptsize}
\begin{tabular}{|c|l|}

\hline
\bfseries Feature& \bfseries Description\\
\hline
$\overline{RR}$&Mean RR intervals\\
\hline
SDNN&Standard deviation of RR intervals\\
\hline
SDSD&Std of successive RR interval differences\\
\hline
RMSSD&Root mean square of successive differences\\
\hline
NN50&\#successive intervals differing more than 50 ms\\
\hline
pNN50&relative amount of NN50\\
\hline
HRVTI&Total number of RR intervals/height of the histogram\\
\hline
TINN&Width of RR histogram through triangular interpolation\\
\hline
\end{tabular}
\end{scriptsize}
  \end{center}
\end{table}
\section{Experimental Evaluation}
In this section, we explain our data collection, available benchmark dataset, baseline methods and evaluation 
\subsection{Datasets and Baseline Methods}
We validate and compare \emph{AutoCogniSys} with baseline methods on both publicly available and our collected datasets 
\subsubsection{RCC Dataset: Collection and Ground Truth Annotation}
For collecting Retirement Community Center Dataset (RCC Dataset), we recruited 22 participants (19 females and 3 males) with age range from 77-93 (mean 85 5, std 3 92) in a continuing care retirement community with the appropriate institutional IRB approval and signed consent  The gender diversity in the recruited participants reflects the gender distribution (85\% female and 15\% male) in the retirement community facility  A trained gerontology graduate student evaluator completes surveys with participants to fill out the surveys  Participants are given a wrist band to wear on their dominant hand, and concurrently another trained IT graduate student have the IoT system setup in participants' own living environment (setup time 15-30 minutes)  The participants are instructed to perform 13 \emph{complex ADLs}  Another project member remotely monitors the sensor readings, videos and system failure status  The entire session lasts from 2-4 hours of time depending on participants' physical and cognitive ability 

We follow the standard protocol to annotate demographics and activities mentioned in the IRB  Two graduate students are engaged to annotate activities (postural, gestural and complex activity) whereas the observed activity performances are computed by the evaluator  Two more graduate students are engaged to validate the annotations on the videos  In overall, we are able to annotate 13 complex activities (total 291 samples) labeling for each participant; 8 hand gestures (total 43561 samples) and 4 postural activities (total 43561 samples) labeling  Annotation of postural and complex activities outcomes no difficulties from recorded videos  However, annotation of hand-gestures is extremely difficult in our scenario  We used video based hand tracker that can track and sketch wrist movements from a video episode \cite{hugo14}  This sketching can help us significantly to identify which particular hand gesture is being performed in the time segment 
\subsubsection{EES Datasets: EDA and PPG Sensor Datasets}
We used Eight-Emotion Sentics (EES) dataset to validate \emph{AutoCogniSys} proposed physiological signal processing approaches \cite{picard01}  The dataset consists of measurements of four physiological signals (PPG/Blood Volume Pulse, electromyogram, respiration and Skin Conductance/EDA) and eight affective states (neutral, anger, hate, grief, love, romantic love, joy, and reverence)  The study was taken once a day in a session lasting around 25 minutes for 20 days of recordings from an individual participant  We consider only PPG and EDA for all of the affective states in our study 
\subsubsection{Baseline Methods}
Though no frameworks ever combined all modalities together into real-time automated cognitive health assessment, we evaluate \emph{AutoCogniSys} performance by comparing the performances of its components individually with upto date relevant works  For hand gesture and postural activity recognition, we consider \cite{alam17} proposed method as baseline  For complex activity recognition, we compare our hand gesture and postural activity classifiers aided HDBN model with three-level Dynamic Bayesian Network \cite{zhu12} framework  For activity performance estimation, activity performance based cognitive health assessment; and EDA and PPG based cognitive health assessment, we have considered \cite{alam16} proposed method as baseline 
\subsection{Activity Recognition Evaluation}
The standard definition for \emph{accuracy} in any classification problem is $\frac{TP+TN}{TP+TN+FP+FN}$ where $TP,TN,FP$ and $FN$ are defined as true positive, true negative, false positive and false negative  For complex activity recognition evaluation, we additionally consider \emph{start/end duration error} as performance metric that can be explained as follows: consider that the true duration of ``cooking'' is 30 minutes (10:05 AM - 10:35 AM) and our algorithm predicts 29 minutes (10 10 - to 10 39 AM)  Then, the start/end duration error is 9 minutes ($|$5 minutes delayed start$|$ + $|$4 minutes hastened end$|$), in an overall error of e g , 30\% (9/30=0 3)  We measure cross-participant accuracy using leave-two-participants-out method for performance metrics, i e , we take out two of the participants' data points from the entire dataset, train our proposed classification models, test the model accuracy on the two left-out participants relevant data points, and continue the process for entire dataset 

\begin{figure*}[!htb]
\begin{minipage}{0 45\textwidth}
\begin{center}
   \epsfig{file=hand_gesture_accuracy pdf,height=1 6in, width=3in}
\caption{Feature Weighted Naive Bayes (FWNB) classification accuracy comparisons with baseline approaches (graphical signatures of all hand gestures are shown) }
   \label{fig:hand_gesture_accuracy}
\end{center}
\end{minipage}
\begin{minipage}{0 29\textwidth}
\begin{center}
\vspace{- 12in}
   \epsfig{file=posture_accuracy_normal pdf,height=1 6in, width=2 1in}
\caption{4-class postural level activity recognition performance and comparisons with baseline method}
   \label{fig:posture_accuracy_normal}
\end{center}
\end{minipage}
\begin{minipage}{0 25\textwidth}
 \begin{center}
 \vspace{- 12in}
   \epsfig{file=posture_accuracy_extended pdf,height=1 6in, width=2 1in}
\caption{6-class diverse postural activity recognition framework accuracy comparisons with the baseline approach }
   \label{fig:posture_accuracy_extended}
\end{center}
 \end{minipage}
\end{figure*}

Fig~\ref{fig:hand_gesture_accuracy} displays Feature Weighted Naive Bayes (FWNB) based the 8-hand gestural activity recognition accuracies comparisons with the baseline methods which clearly depicts the outperformance of our method (5\% improvement) with an overall accuracy of 92\% (FP rate 6 7\%) in RCC dataset  For postural activity recognition, dataset achieving 91\% postural activity recognition accuracy (FP rate 9 5\%) which outperforms the baseline approach significantly (8\% improvement)  Now, we expand the postural activities for RCC datasets into 3 diverse `walking' postures: `normal walking', `walking with walker', `walking with single stick' and the accuracy goes down to 88\% (FP 7 9\%)  Fig ~\ref{fig:posture_accuracy_normal} and Fig ~\ref{fig:posture_accuracy_extended} illustrate 4-class postural and extended 6-class postural classifier accuracies respectively which clearly posit that \emph{AutoCogniSys} outperforms in each case of postural activities as well as overall performances (8\% and 7\% improvement respectively) 

For complex activity classification, we choose RCC dataset to train our HDBN model  Our leave-two-participants out method results an accuracy of 85\% (FP Rate 3 6\%, precision 84 2\%, recall 84 5\%, ROC Area 98 2\%) with a start/end duration error of 9 7\%  We run the entire evaluation for baseline complex activity recognition algorithm too achieving an overall accuracy of 78\% (FP Rate 5 2\%, precision 79 6\%, recall 78 5\%, ROC Area 82 7\%) which is clearly lower performed method than our approach  Fig  \ref{fig:complex_activity_roc} and Fig~\ref{fig:complex_activity_accuracy} illustrate the ROC curve and each complex activity recognition accuracy comparisons with baseline method which depict the outperformance of our framework over baseline methods (7\% improvement)  Fig~\ref{fig:complex_activity_accuracy} also shows that inclusion of postural activity improves the final complex activity recognition (4\% improvement) 
 \begin{figure}  [!htb]
  \begin{minipage}{0 15\textwidth}
 \begin{center}
   \epsfig{file=complex_activity_roc pdf,height=1 4in, width=1 1in}
\caption{ROC curve for complex activity recognition}
   \label{fig:complex_activity_roc}
\end{center}
\end{minipage}
\begin{minipage}{0 33\textwidth}
\begin{center}

   \epsfig{file=complex_activity_accuracy pdf,height=1 4in, width=2 3in}
\caption{Complex ADLs recognition accuracy improvement and comparison with baseline \cite{zhu12} and HMM based method}
   \label{fig:complex_activity_accuracy}
\end{center}

\end{minipage}
\end{figure}

\subsection{Quantification of Performance Score}
To characterize both the qualitative and quantitative health assessment performance scores, we start with four different feature groups ranging from both functional and physiological health measures: (i) observation based activity features, (ii) automatic activity performance features, (iii) EDA features and (iv) PPG features 

In \emph{observation based activity features}, we design a complex activity set comprised of multiple subtasks which are involved with task {\it interruption, completion and sequencing}  Participants are instructed to perform the complex activities while the trained evaluator observed the aforementioned functional activity performance measures  Each incorrect attempt of performance measure will be assigned one point thus higher score reflects lower performance of functional activities \cite{dawadi14}  We first detect hand gesture and postural activities  Then, we feed the low-level activity contexts (gestural and postural) combined with ambient contexts (object and ambient motion sensor readings) into HDBN for single inhabitant model \cite{alam16b} to recognize complex activities  The complex activity recognition framework provides both activity labels and activity window (start-end points)  Then, we extract features of object sensor, ambient sensor, gestural activity and postural activity events for each activity window  The features are number of occurrences, mean number of occurrences, consecutive 1, 2, 3, $\ldots$ 20 occurrences, top 10, 20, 30, $\ldots$, 90 percentile etc (29 features in total)  In \emph{physiological features} we first detect 13 complex activities using HDBN algorithm which provides activity labels and activity window (start-end points), apply noise reduction, motion artifacts removal, extract 7 EDA features and 8 HRV features for each activity and take the mean of them over time (minutes) to get 15 (7+8) complex activity physiological features set for each participant  In summary, we extract 3 observation based activity features, 29 automatic activity performance features, 7 EDA features and 8 HRV features 
\subsection{Physiological Signal Processing Performance Evaluation}
Standard evaluation technique should use both experimental and publicly available datasets to confirm the outperformance of the novel approaches  We first evaluate our physiological signal processing techniques using a publicly available dataset (EES Dataset \cite{picard01}) to detect 8 human emotions  Then, in next section, we evaluate our methods in assessing cognitive health status of older adults using RCC dataset 

For EDA, we first apply SWT method to remove motion artifacts and noises  Then, we use cvxEDA method to separate tonic and phasic components of EDA  Then, we extract 7 EDA features on a sliding window of 4 seconds  Finally, we feed the 7 EDA features into a SMO based SVM algorithm \cite{cao06}  We use 10-fold cross validation to classify eight emotions achieving 87\% of overall accuracy (FP rate 6\%)  For PPG, we first apply our proposed PMAF based noises and motion artifacts removal technique  Then, we calculate HRV and perform time-domain feature extraction to extract 8 HRV features on a sliding window of 4 seconds  We feed these features into a SMO based SVM algorithm \cite{cao06}  Our 10-fold cross validation shows accuracy of 79\% (FP rate 11 5\%) of detecting 8 emotions on EES Dataset  Fig  \ref{fig:ees_eda} and Fig  \ref{fig:ees_ppg} clearly depict that \emph{AutoCogniSys} proposed EDA and PPG signal processing techniques significantly improve the accuracy over the baseline \cite{alam16} method (10\% and 12\% improvement) 

\begin{figure}[!htb]
\begin{minipage}{0 24\textwidth}
\begin{center}
   \epsfig{file=ees_eda pdf,height=1 2in, width=1 8in}
\caption{(EES Databaset) EDA features based Eight Emotion classification accuracy comparisons with baseline method}
   \label{fig:ees_eda}
\end{center}
\end{minipage}
\begin{minipage}{0 23\textwidth}
\begin{center}
   \epsfig{file=ees_ppg pdf,height=1 2in, width=1 7in}
\caption{(EES Dataset) PPG features based 8-Emotion classification accuracy comparisons with baseline method}
   \label{fig:ees_ppg}
\end{center}
\end{minipage}

\end{figure}
\subsection{Evaluation of Performance Scores}
The feature subsets used in the experimentation for observation and survey based clinical assessments and technology guided physiological and activity initiated health assessments are depicted in Table~\ref{tab:feature_subset}  From our 6 demographics surveys, we find significant distributions in terms of cognition only for SLUMS Score (S-Score)  Based on that, we divide our participants pool into three groups: \emph{Not Cognitively Impaired (NCI), Mild Cognitively Impaired (MCI) and Cognitively Impaired (CI)} where the number of participants are $5$, $7$ and $10$ respectively 
\begin{table}[!t]
\begin{scriptsize}


{\centering 
\renewcommand{\arraystretch}{ 6}
\caption{Feature Subsets}
\label{tab:feature_subset}
\begin{tabular}{|l|L{5 5cm}|}
\hline
\bfseries Feature& \bfseries Description\\
\hline
Observation & Task Completeness (TC), Sequencing (SEQ), Interruptions (INT)\\
\hline
Survey & SLUMS Score (S-Score), ZUNG Score (Z-Score), IADL Score (I-Score), Yale Score (YPAS), Barthel Score (B-Score), GDS Score (G-Score)\\
\hline
EDA  and HRV & 7 and 8 Features\\
\hline
Activity Performance& Supervised (TC, SEQ, INT), Unsupervised\\
\hline
Arousal& EDA and HRV features of each complex activity window\\
\hline

\end{tabular}
}
\end{scriptsize}
\end{table}


\begin{figure}[!htb]
\begin{center}
   \epsfig{file=group_correlation pdf,height=1in, width=3 3in}
\caption{\emph{AutoCogniSys} Proposed Method Based Group Correlation analysis ( $r-value$) NCI, MCI and CI represent not cognitive, mild cognitive and cognitively impaired group of population  TC, INT, SEQ, EDA and HRV represent task completeness, interruption scores, sequencing scores, electrodermal activity features and heart rate variability features}
   \label{fig:group_correlation}
\end{center}
\vspace{- 2in}
\end{figure}
\begin{figure}[!htb]
\begin{center}
   \epsfig{file=group_correlation_baseline pdf,height=1in, width=3 3in}
\caption{Baseline \cite{alam16} method based Group Correlation analysis ( $r-value$)}
   \label{fig:group_correlation_baseline}
   \vspace{- 25in}
\end{center}
\end{figure}

\subsection{Statistical Correlation Analysis of Cognitive Health}
We used Pearson correlation coefficients with significance on $p<0 05$* for individual feature and partial correlation coefficients with significance on $p<0 005$** for group of features correlation analysis  Fig  \ref{fig:group_correlation} and Fig  \ref{fig:group_correlation_baseline} show the group correlation analysis results based on \emph{AutoCogniSys} proposed framework and baseline \cite{alam16} framework respectively  It can be clearly depicted that our proposed framework improves the correlation with the ground truths 
\subsection{Machine Learning Classification of Cognitive Health}
We evaluate using machine learning classifiers to predict cognitive status of older adults using both individual modalities and combined features  We use leave-two-participants out method to train and test classification accuracy 

We first choose the individual activity features (machine learning method based interruption scores, sequencing scores, unsupervised scores) and their combined features to train and test cognitive impairment status classification for SMO based SVM algorithm \cite{cao06}  The classification accuracies are 72\%, 69\%, 76\% and 83\% respectively  Then we consider 7 EDA-activity features and 8 HRV-activity features individually in training and testing phase of SMO based SVM algorithm \cite{cao06} resulting 85\% and 80\% accuracy respectively 

\begin{figure}[!htb]
\begin{minipage}{0 24\textwidth}
\begin{center}
   \epsfig{file=combined_classification pdf,height=1 2in, width=1 7in}
   \vspace{- 15in}
\caption{Individual and combined classification accuracies comparison with baseline method for cognitive impairment status detection}
   \label{fig:combined_classification}
\end{center}
\end{minipage}
\begin{minipage}{0 23\textwidth}
\begin{center}
   \epsfig{file=each_activity_cognitive_assessment pdf,height=1 2in, width=1 7in}

\caption{Machine learning based cognitive health assessment accuracy for each complex activity in terms of activity, EDA and HRV features }
   \label{fig:each_activity_cognitive_assessment}
\end{center}
\end{minipage}
\end{figure}

For combined classifier, we first applied sequential forward feature selection to find the best combinations of 1- 3 features for cognitive impairment classification group MCI, NCI and CI in terms of combined activity features (29 features), EDA-activity features (7 features) and HRV-activity features (8) features  Our final combined classifier (SMO based SVM algorithm \cite{cao06}) provides an accuracy of {\bf 93\%} in detecting the cognitive impairment status of older adults  Fig  \ref{fig:combined_classification} shows our proposed individual and combined methods outperform the baseline \cite{alam16} significantly (13\% improvement)  Fig  \ref{fig:each_activity_cognitive_assessment} shows the cognitive impairment status prediction accuracy for each modality (activity feature, EDA and HRV) per individual complex activity 
\subsection{Discussion}
If we exclude the postural activities from automated activity performance scoring, we find reduced statistical correlation with original task completeness performance for \{NCI, MCI, CI\} participant group (INT 0 53*, SEQ 0 21' and unsupervised 0 49')  However, if we skip our proposed motion artifact removal stage, we find reduced statistical correlation with \{NCI, MCI\} and \{MCI, CI\} groups of participants (EDA and HRV correlations respectively \{0 51*, -0 51*\} and \{-0 53*,0 46\})  To test our proposed motion artifacts removal impact on EDA signals more rigorously, we choose 5 random participants, engage one expert motion artifact annotator to annotate motion artifacts segment on each participant's first 30 minutes of complex dataset using recorded video and apply both baseline and our methods to detect motion artifact segments  While baseline method achieves 75 5\% (FP rate 20 3\%) accuracy in detecting motion artifact segments, \emph{AutoCogniSys} outperforms achieving 89 9\% (FP rate 8 9\%) accuracy  In terms of experience, we have seen 100\% acceptance of wearing wrist-band,  71\% of acceptance for signing consent on using cameras and 0\% failure rate of collecting continuous data 
\section{Conclusion}
We propose, \emph{AutoCogniSys}, an IoT inspired design approach combining wearable and ambient sensors embedded smart home design, extensive signal processing, machine learning algorithms and statistical analytics to automate cognitive health assessment in terms of complex activity performances and physiological responses of daily events  Additionally, our postural activity detection approach in diverse population cum improved activity performance measurement and fundamental physiological sensor artifacts removal from physiological sensors help facilitate the automated cross-sectional cognitive health assessment of the older adults  Our efficient evaluation on each modality (physical, physiological and ambient) and each activity mode proves that any of the mode (say single activity and single sensor) also can provide significant improved cognitive health assessment measure 




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❷ 手柄开机 ❸ 头盔开机 ❹ 佩戴头盔，调节至清晰舒适的位置 首次开机：拔出绝缘纸，手柄自动开机（蓝灯闪烁） 非首次开机：短按手柄 Home 键开机（蓝灯闪烁） 长按头盔电源键 2 秒（蓝灯常亮） 调节旋钮转动绑带，使后脑垫套在头上，微调绑带长度及佩戴位置至视野清晰 04 提示：近视用户请佩戴眼镜或镜片插件使用，本产品不具备近视调节功能。 CN
❺ 微调顶绑带 微调顶绑带使其受力以减少额头压力 ❻ 瞳 距 调 节 在系统设置：“设置” ► “显示”界面中进行瞳距调节，点击“+”或“-”按钮可微调瞳距直至画面清晰 64mm 请勿 强行 掰动镜 筒，以 免造 成损坏 ！ 请注 意设 置使用 不合适 的瞳 距，可 能 会引起 视 觉重影 或 者眼睛 疲 劳。准 确 的瞳距 设 置有助 于 获得清 晰 的图像 并 减少眼睛 疲劳。 05 CN
产品部件详情说明 头盔状态指示灯 蓝灯常亮：开机进行中或工作状态 黄灯常亮：充电中，电量低于 98% 红灯常亮：充电中，电量低于 20% 绿灯常亮：充电完毕，电量大于 98% 或 充满 蓝灯闪烁：关机进行中 红灯闪烁：电量低于 20% 指示灯熄灭：休眠或关机 06 ① 电源键 开机：长按 2 秒 关机：长按 5 秒 复位：长按 10 秒 开机时，短按休眠 ② ③ ④ ⑤ 状态指示灯 贴脸泡棉 音量键 彩色透视摄像头 使用时请勿遮挡 ⑥ ⑦ ⑧ 顶部绑带 可拆卸 绑带旋钮 环境追踪摄像头 使用时请勿遮挡 ⑨ ⑩ ⑪ USB-C 接口 左 / 右喇叭 距离传感器 佩戴头盔后，系统自动唤醒 摘下头盔后，系统自动休眠 ⑫ ⑬ 眼球追踪摄像头 此功能仅 Pro 版支持 使用时请勿遮挡 面部追踪摄像头 此功能仅 Pro 版支持 使用时请勿遮挡 CN
手柄状态指示灯 熄灭：已连接或者关机 蓝灯常亮：固件升级模式 蓝灯闪烁：连接中 红蓝灯交替慢速闪烁：等待配对 ① ② 摇杆 菜单键 ③ Home 键 开机 : 短按关机 : 长按 6 秒退出应用 : 短按屏幕中心校正 : 长按 1 秒④ ⑤ ⑥ ⑦ 状态指示灯 抓握键 截屏键 扳机键 ⑧ ⑨ 电池盒 打开：拨动拨钮，电池盒弹出 安装：按压直至自动锁紧 追踪光环 使用时请勿遮挡 注：手柄挂绳可按图示将粗绳穿过细绳并锁紧在手柄尾端 07 CN
手柄硬件复位 如果手柄出现按 Home 键和任何按键均无反应或者头盔中虚拟手柄卡死不动的问题可拆装电池重新启动手柄。 近视用户配戴 本设备不具备近视调节功能，头盔可支持佩戴镜框宽度小于 150mm 的大多数标准眼镜。 操作说明 头控模式 未连接手柄的情况下，您可通过转动头部光标及点击头盔音量加减按键进行操作。 切换主控手柄射线 在主控菜单下，短按对应手柄的扳机键可以切换主控手柄的射线。 屏幕中心校正 戴着头盔直视前方，按住手柄 Home 键（或头控模式下头盔上的音量减键）1 秒以上，进行屏幕中心的校正将菜 单拉到当前视野朝向位置。 断开手柄 长按手柄 Home 键直至手柄状态指示灯红灯亮起并伴随振动产生时即可松手，此时手柄关机并断开与头盔的连接。 您无需刻意进行手柄关机操作，在以下状态下手柄会自动关机省电： •头盔进入深度休眠时（摘下头盔后一段时间） •头盔手柄管理界面解绑手柄时 •头盔关机时 添加新手柄 如需添加新手柄（头盔最多可同时连接一对手柄，即左右手柄各一只），或解绑手柄后再次连接 , 可进入“设置” “手 柄”，点击“配对”，同时按住手柄 Home 键和扳机键直至手柄状态指示灯红蓝交替闪烁时即可松开，然后根据 头盔画面提示操作。 ► 休眠 / 唤醒 方式一：摘下头盔一段时间后，系统自动休眠；戴上头盔时，系统自动唤醒。 方式二：短按头盔电源键也可以进行休眠或唤醒操作。 硬件复位 头盔硬件复位 如果头盔出现短按头盔电源键没有反应或头盔的画面卡死等问题，可以长按头盔电源键 10 秒以上重新启动头盔。 08 CN
安装眼镜支架 安装遮光鼻托 如您存在眼镜摩擦光学镜片或者压迫鼻梁的问题，请按照图示安装眼镜支架以增加间隔空间。 您可根据佩戴的舒适度选择是否安装。 如您感觉鼻子处漏光影响体验，请按照图示安装遮光鼻托配件。 由于眼睛空间密闭可能加剧起雾及出汗问题，您可根据喜好选择是否安装。 ❶ 摘下贴脸泡棉 ❷ 将眼镜支架按照图示安装在产品上 ❸ 将贴脸泡棉按照图示安装眼镜支架上 ❶ 摘下贴脸泡棉 ❸ 安装贴脸泡棉❷ 将遮光鼻托按照图示方式安装在贴脸泡棉上 注：按照图示拆卸眼镜支架 09 CN
更换贴脸泡棉 贴脸泡棉多次清洁和长时间使用后会变色和质地变软，您可酌情更换新泡棉。 更换顶绑带 摘下贴脸泡棉 ❸ 安装贴脸泡棉 按照图示捏住顶绑带金属扣，往下压到底然后抽出 ❷ •购买优质热门应用 •畅 聊 社 区， 与 众 多 PICO 玩 家 一起探索 VR 世界 •管理设备更便捷 •参与丰富互动活动 •更多精彩内容等你来发现 ❶ 微 信公 众 号：PICO VR抖音：PICO官 方 旗 舰 店哔 哩 哔 哩：PICO-VR官 方微 博：PICO-VR ❶ ❷ 10 CN
In The Box: VR Headset / 2 Controllers / 4 1 5V AA Alkaline Batteries / Glasses Spacer / Nose Pad / 2 Controller Lan- yards / USB-C Power Adapter / USB-C to C 2 0 Data Cable / Quick Guide / User Guide / Safety and WarrantyGuide Important Health & Safety Notes • This product is designed and intended to be used in an open and safe indoor area, free of anytripping or slipping hazards  To avoid accidents, remain conscious to the potential confines ofyour physical area and respect the boundary of your virtual area whenever you see it  Be sure towear the lanyards when using the Controllers  Make sure that there is enough space around yourhead and body (at least 2 meters by 2 meters) to stretch your arms to avoid damage or injury toyourself, others, and your surroundings  • This product is not recommended for children aged 12 and under  It is recommended to keep headsets,controllers and accessories out of the reach of children  Teenagers aged 13 and over must use it underadult supervision to avoid accidents  • This product is designed to accommodate most prescription glasses  Make sure to wear the VR Headsetin a manner in which the VR Headset lenses do not rub or impair your prescription lenses  • Prolonged use may cause dizziness or eye fatigue  It is recommended to take a break every 30 minutes Try relieving your eyestrain by looking at distant objects  If you feel any discomfort, stop using the prod- uct immediately  If the discomfort persists, seek medical advice • Do not expose the optical lenses to direct sunlight or other strong light sources  Exposure to directsunlight may cause permanent yellow spot damage on the screen  Screen damage caused by sunlightexposure or other strong sources of light is not covered by the warranty  • This product supports interpupillary distance (IPD) adjustment in system settings  When adjusting,please be aware that with the minimum IPD, it may touch the bridge of the nose  You can adjust the IPDaccording to your actual interpupillary distance in "Settings"►"Display"  Please note that using inap- propriate IPD may increase the risk of discomfort  • This product has an “Eye Protection Mode”, certified by TÜV Rheinland (Germany), which can protectyour eyes by reducing blue light in the three color channels using software algorithms  The screen ap- pears yellowish in this mode and you can turn this feature on/off in "Settings"►"Display"►"Color"►“- Eye Protection”  • Protect optical lenses during use and storage to prevent damage, such as scratches or exposure tostrong light or direct sunlight  * Product and packaging are updated regularly, and the functions and contents of the standalone headset may be upgraded in the future Therefore, the content, appearance and functionality listed in this manual and product packaging are subject to change and may notreflect the final product  These instructions are for reference only  * Carefully read this user guide before using the product and share this information with any other users, as it contains important safetyinformation  Keep the user guide as reference for the future  11 EN
6 Degrees of Freedom VR The device can track your translational and rotational movements in all directions (up/down, left/right,forward/backward, pitch, roll, and yaw)  Your movements in the real world will be captured and translatedto what you see in the virtual world when using the appropriate content  Ensure a safe environment before you start your VR experience  1  Clear a safe indoor area of at least 2 meters by 2 meters  Keep the room bright, avoid spaces with main- ly single-colored walls, glass, mirrors, moving pictures or other similar objects  2  Remove the protective film that covers the headset front cameras  Wear the lanyards connected to theControllers  3  Set up your environment by following instructions on the VR Headset screen  Install Batteries ❶Pull the tab to remove the insulating paper  Quick Guide 2 m 2m This product can not guarantee your safety with guardian system, you will need to always pay attention to the surrounding safety  * Note: 1 5V AA alkaline batteries should be used



ground_truth: Wearable sensors.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What is the recommended space for using the VR headset?

Read the following text and answer briefly.

Context 2: Slide the toggle according to arrow direction toopen the battery case  12 EN
Power on the Controller ❷ First Start: The Controller will start automaticallyafter removing the insulating paper  Others: Short press the Home button for 1second until the status indicator flashes blue Power on the VR Headset ❸ Long press the Power button for 2 seconds untilthe status indicator turns blue Wear Your Headset for a Comfortable Fit and View ❹ Adjust the strap dial to turn the strap so that the back of your head rests on the padding  Fine-tune thelength and position of the strap to give a clear view  * Note: You can use this product with prescription glasses or lenses insert  13 EN
Fine-tune the Top Strap ❺ Fine-tune the head strap to reduce pressure on the forehead  Interpupillary Distance (IPD) Adjustment ❻ In System Setting, go to “Setting” ► “Display” to adjust IPD, tap “+” or “-” button to slightly adjust IPDuntil the picture is clear  14 64mm Please note that inappropriate IPD setting may cause ghosting or eyestrain Accurate IPD setting helps you get a clear imaging and ease eyestrain  EN
Product Details VR Headset Status Indicator Legend Blue: Powered on with battery over 20% Yellow: Charging: Battery is less than 98% Red: Charging: Battery is less than 20% Green: Charging: Battery is more than 98% or charge complete Blue flashing: Shutting down Red flashing: Battery is less than 20% Off: Sleeping or Powered off Power Power on: Long press for 2 seconds Power off: Long press for 5 seconds Hardware reset: Long press for 10 seconds Short press to enter sleep or wake up Status Indicator Face Cushion Volume ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ RGB See Through Camera Do not block during use  Top Strap Removable Strap Dial Tracking Cameras Do not block during use  ⑨ ⑩ ⑪ USB-C Interface Left/Right Speaker Proximity Sensor The system wakes upwhen the VR headset isput on, sleeps when VRheadset is taken off  ⑫ ⑬ Eye Tracking Cameras Pro version only  Do not block during use  Face Tracking Camera Pro version only  Do not block during use  15 EN
Controller Status Indicator Legend Off: Connected or Powered off Blue: Firmware updating in progress Blue flashing: Searching for connection Red and blue flashing alternately: Pairing in progress 16 Joystick Menu ③ ① ② Home Power on: Short pressPower off: Long press for 6 secondsReturn home screen: Short pressScreen recentering: Press for 1 secondStatus Indicator Grip Capture Trigger ④ ⑤ ⑥ ⑦ ⑧ ⑨ Battery Case Open: Slide down the toggle andpop up the battery case  Lock: Push the battery case to lock  Tracking Ring Do not block during use  * Note: Pass the Controller Lanyardthrough the string as shown andlock at the end of the Controller EN
Operating Instructions Headset Control Mode If the Controller is not connected, you can interact with the home screen by moving your head to directthe crosshairs over your intended selection and clicking the Volume Up/Down button on the VR Headset  Switch the pointer of the master Controller In the home screen, short press the Trigger of the corresponding Controller to switch the pointer of themaster Controller  Screen re-centering Wear the VR Headset and look straight ahead, press and hold the Home button of the Controller or VRHeadset ( or the Volume Down button of the VR Headset in head control mode) for more than 1 second tore-center the screen  Disconnect the Controller Press and hold the Home button until the status indicator turns red and the Controller vibrates Controllers will automatically shut down to save power in the following cases:When the VR Headset enters deep sleep (a while after the VR Headset is taken off)When the Controller is unpairedWhen the VR Headset is powered off Add a new Controller If you need to add a new Controller (the VR Headset can only connect one left Controller and one rightController) or reconnect with an unpaired Controller  Go to “Settings” ► “Controller”, click on “Pair” Press and hold the Home button and the Trigger of the Controller at the same time until the red and bluelights of the Controller flashing alternately, and then follow the instructions on the VR Headset screen  Sleep / Wake up Option 1 (Proximity Sensor) Take off VR Headset for automatic sleeping: wear the VR Headset for automat- ic waking up  Option 2 (POWER Button) Press the Power button of the VR Headset for manual sleeping or waking up  Hardware reset VR Headset reset If the visual in the VR Headset freezes, or the VR Headset does not respond after short press the Powerbutton, you can press the Power button of the VR Headset for more than 10 seconds to reboot the VRHeadset  Controller reset If the virtual Controller, the Home button or any buttons of the Controller doesn\'t respond, remove andreinstall the battery case to restart the Controller  The VR Headset Adjustment This device has no myopia adjustment function  The VR Headset allows wearing most standard glasseswith a frame width of less than 150mm  to install Glasses Spacer to increase the space  You can install or not according to your situation  17 EN
Install Glasses Spacer Install Nose Pad If you have glasses collision with headset lens or pressure on the bridge of nose, please follow the pictureto install Glasses Spacer to increase the space  You can install or not according to your situation  If you feel light leaking from your nose, please follow the picture to install Nose Pad to block the light You can consider having it installed at your own discretion  Disassemble the Face Cushion  Install the Glasses Spacer on the Headset  ❸ ❶ ❷ Install the Face Cushion on the Glasses Spacer  Disassemble the Face Cushion  Install the Nose Pad on the Face Cushion  ❶ ❷ Install the Face Cushion on the Headset  ❸ * Note: Disassemble the Glasses Spacer 18 EN
Replace Face Cushion The Face Cushion will have the following phenomena such as color change, surface fluff, soft texture afterlong-term use and repeated cleaning  You can replace a new Face Cushion as needed  Replace Top Strap ❶ ❷ Disassemble the Face Cushion  Pinch the metal buckle of the top strap asshown, press it down and pull it out Install the Face Cushion on  ❸ ❷ ❶ • Purchase high-quality and trending apps • Join PICO Community and explore the VR worldwith other PICO players• Manage your device with ease • Engage in diverse and interactive activities • More exciting features waiting for you 19 EN
'



ground_truth: It is recommended to have at least a 2x2 meter space for using the VR headset.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What was the population of McPherson County according to the 2020 census?

Read the following text and answer briefly.

Context 3: McPherson County (standard abbreviation: MP) is a county located in the U S  state of Kansas   As of the 2020 census, the county population was 30,223  The largest city and county seat is McPherson  The county is named for Civil War General James B  McPherson 

History

Early history

For many millennia, the Great Plains of North America was inhabited by nomadic Native Americans  From the 16th century to 18th century, the Kingdom of France claimed ownership of large parts of North America  In 1762, after the French and Indian War, France secretly ceded New France to Spain, per the Treaty of Fontainebleau  In 1802, Spain returned most of the land to France, but keeping title to about 7,500 square miles 

In 1803, most of the land for modern day Kansas was acquired by the United States from France as part of the 828,000 square mile Louisiana Purchase for 2 83 cents per acre  In 1848, after the Mexican–American War, the Treaty of Guadalupe Hidalgo with Spain brought into the United States all or part of land for ten future states, including southwest Kansas  In 1854, the Kansas Territory was organized, then in 1861 Kansas became the 34th U S  state 

19th century

From the 1820s to 1870s, the Santa Fe Trail passed through, what is now McPherson County  The trail entered the county, east of Canton, then south of Galva, then north of Inman, and west towards Lyons  In 1855, Charles O  Fuller established a ranch adjacent to the Running Turkey Creek Crossing about two miles south and one mile east of Galva  Fuller's Ranch provided accommodations for travelers on the Santa Fe Trail and was probably the first white settlement in McPherson County 

Peketon County was established in 1860, by the passage of a bill by S  N  Wood:  An act to establish Peketon County  Section 1  - That all that territory west of the sixth principal meridian and south of Township 16, in Kansas Territory, be and the same is hereby erected into a county, to be known by the name of Peketon County  On February 17, 1865, Peketon County was abolished, and McPherson County was made a part of Marion County, which extended from the west line of Chase County to the present western boundary of Kansas 

In 1868, Solomon Stephens and L  N  Holmberg were appointed Justices of the Peace—the first officers in what is now McPherson County  The next year (1869) occurred the first election for the township, now the county of McPherson  McPherson was regularly organized as a county in the spring of 1870, a mass meeting being held at Sweadal  Sweadal, the county seat thus selected, was located about one mile and a half southwest of the present site of Lindsborg  In September, however, the County Commissioners resolved to meet at the latter place, McPherson which had already been located some two years 

In April, 1873, a petition was filed for the county seat re-location  It was signed by 483 voters, and a special election was accordingly ordered for June 10  Upon that day, McPherson received 605 votes, New Gottland 325, King City 3 and Lindsborg 1; McPherson's majority over all, 276  In May the McPherson Town Company had offered, as an inducement for the location of the county seat at this point, the free use of rooms for ten years, and the donation of two squares of land on the town site  The offer was accepted the next month, the County Commissioners selecting blocks 56 and 65  Thus the county seat was established at McPherson and has remained since 

As early as 1875, city leaders of Marion held a meeting to consider a branch railroad from Florence  In 1878, Atchison, Topeka and Santa Fe Railway and parties from Marion County and McPherson County chartered the Marion and McPherson Railway Company   In 1879, a branch line was built from Florence to McPherson, in 1880 it was extended to Lyons, in 1881 it was extended to Ellinwood  The line was leased and operated by the Atchison, Topeka and Santa Fe Railway  The line from Florence to Marion, was abandoned in 1968  In 1992, the line from Marion to McPherson was sold to Central Kansas Railway  In 1993, after heavy flood damage, the line from Marion to McPherson was abandoned  The original branch line connected Florence, Marion, Canada, Hillsboro, Lehigh, Canton, Galva, McPherson, Conway, Windom, Little River, Mitchell, Lyons, Chase, then connected with the original AT&SF main line at Ellinwood 

In 1887, the Chicago, Kansas and Nebraska Railway extended its main line from Herington to Pratt   This main line connected Herington, Ramona, Tampa, Durham, Waldeck, Canton, Galva, McPherson, Groveland, Inman, Medora, Hutchinson, Whiteside, Partridge, Arlington,  Langdon, Turon, Preston, Natrona, Pratt   In 1888, this main line was extended to Liberal   Later, this line was extended to Tucumcari, New Mexico and Santa Rosa, New Mexico, where it made a connection with the Southern Pacific from El Paso, Texas   The Chicago, Kansas and Nebraska Railway was absorbed by the Chicago, Rock Island and Pacific Railway  This line is also called the "Golden State Route" 

20th century
The National Old Trails Road, also known as the Ocean-to-Ocean Highway, was established in 1912, and was routed through Windom, Conway, McPherson 

Geography

According to the U S  Census Bureau, the county has a total area of , of which  is land and  (0 3%) is water 

Adjacent counties
 Saline County (north)
 Dickinson County (northeast)
 Marion County (east)
 Harvey County (southeast)
 Reno County (southwest)
 Rice County (west)
 Ellsworth County (northwest)

Major highways
  Interstate 135
  U S  Route 56
  U S  Route 81
  K-4
  K-61
  K-153

Demographics

The McPherson Micropolitan Statistical Area includes all of McPherson County 

2000 census
As of the census of 2000, there were 29,554 people, 11,205 households, and 7,966 families residing in the county   The population density was 33 people per square mile (13/km2)   There were 11,830 housing units at an average density of 13 per square mile (5/km2)   The racial makeup of the county was 96 53% White, 0 81% Black or African American, 0 34% Native American, 0 32% Asian, 0 06% Pacific Islander, 0 79% from other races, and 1 16% from two or more races   1 94% of the population were Hispanic or Latino of any race  37 1% were of German, 12 9% Swedish, 12 1% American, 6 7% English and 6 3% Irish ancestry according to Census 2000 

There were 11,205 households, out of which 33 00% had children under the age of 18 living with them, 62 50% were married couples living together, 6 00% had a female householder with no husband present, and 28 90% were non-families  25 50% of all households were made up of individuals, and 11 80% had someone living alone who was 65 years of age or older   The average household size was 2 49 and the average family size was 2 99 

In the county, the population was spread out, with 25 40% under the age of 18, 10 30% from 18 to 24, 25 20% from 25 to 44, 21 80% from 45 to 64, and 17 30% who were 65 years of age or older   The median age was 38 years  For every 100 females there were 95 90 males   For every 100 females age 18 and over, there were 92 90 males 

The median income for a household in the county was $41,138, and the median income for a family was $48,243  Males had a median income of $33,530 versus $21,175 for females  The per capita income for the county was $18,921   About 4 20% of families and 6 60% of the population were below the poverty line, including 5 20% of those under age 18 and 8 10% of those age 65 or over 

Government

Presidential elections
McPherson county is often carried by Republican candidates  The last time a Democratic candidate has carried this county was in 1964 by Lyndon B  Johnson 

Laws
Following amendment to the Kansas Constitution in 1986, the county remained a prohibition, or "dry", county until 1996, when voters approved the sale of alcoholic liquor by the individual drink with a 30 percent food sales requirement 

Education

Colleges
 McPherson College in McPherson
 Bethany College in Lindsborg
 Central Christian College in McPherson

Unified school districts
 Smoky Valley USD 400
 McPherson USD 418
 Canton-Galva USD 419
 Moundridge USD 423
 Inman USD 448

School district office in neighboring county
 Goessel USD 411
 Little River-Windom USD 444

Museums
 Birger Sandzén Memorial Gallery in Lindsborg
 McCormick-Deering Days Museum in Inman
 McPherson Museum in McPherson
 Lindsborg Old Mill & Swedish Heritage Museum in Lindsborg
 Kansas Motorcycle Museum in Marquette

Communities

Cities

 Canton
 Galva
 Inman
 Lindsborg
 Marquette
 McPherson (county seat) 
 Moundridge
 Windom

Unincorporated communities
† means a Census-Designated Place (CDP) by the United States Census Bureau 
 Conway
 Elyria†
 Groveland
 Johnstown
 New Gottland
 Roxbury†

Ghost towns
 Alta Mills
 Battle Hill
 Christian
 Doles Park
 Elivon
 King City
 Sweadal

Townships
McPherson County is divided into twenty-five townships   The cities of Lindsborg and McPherson are considered governmentally independent and are excluded from the census figures for the townships   In the following table, the population center is the largest city (or cities) included in that township's population total, if it is of a significant size 

See also
 List of people from McPherson County, Kansas
 National Register of Historic Places listings in McPherson County, Kansas
 McPherson Valley Wetlands
 Maxwell Wildlife Refuge

References

Notes

Further reading

 Wheeler, Wayne Leland  "An Analysis of Social Change in a Swedish-Immigrant Community: The Case of Lindsborg, Kansas " (PhD dissertation, University of Missouri-Columbia; ProQuest Dissertations Publishing, 1959  5905657) 

County
 Through the Years: A Pictorial History of McPherson County; McPherson Sentinel' Heritage House Publishing Co; 1992 
 McPherson County First Courthouse Built About 1869 or 1870; Lindsborg News-Record; March 30, 1959 
 Pioneer Life and Lore of McPherson County, Kansas; Edna Nyquist; Democratic-Opinion Press; 1932 
 A History of the Church of the Brethren in Kansas (includes McPherson College history); Elmer LeRoy Craik; McPherson Daily; Republican Press; 397 pages; 1922 
 Portrait and Biographical Record of Dickinson, Saline, McPherson, and Marion Counties, Kansas; Chapman Bros; 614 pages; 1893 
 Standard Atlas of McPherson County, Kansas; Geo  A  Ogle & Co; 82 pages; 1921 
 Plat Book of McPherson County, Kansas; North West Publishing Co; 50 pages; 1903 
 Edwards' Atlas of McPherson County, Kansas; John P  Edwards; 51 pages; 1884 

Trails
 The Story of the Marking of the Santa Fe Trail by the Daughters of the American Revolution in Kansas and the State of Kansas; Almira Cordry; Crane Co; 164 pages; 1915  (Download 4MB PDF eBook)
 The National Old Trails Road To Southern California, Part 1 (LA to KC); Automobile Club Of Southern California; 64 pages; 1916  (Download 6 8MB PDF eBook)

Mennonite Settlements
 Impact of Mennonite settlement on the cultural landscape of Kansas; Brenda Martin; Kansas State University; 1985/1988  
 Mennonite settlement : the relationship between the physical and cultural environment; Susan Movle; University of Utah; 1975/1886 
 Status of Mennonite women in Kansas in their church and home relationships; Eva Harshbarger; Bluffton College; 1925/1945 

External links

County
 
 McPherson County - Directory of Public Officials
Historical
 , from Hatteberg's People'' on KAKE TV news
Maps
 McPherson County Maps: Current, Historic, KDOT
 Kansas Highway Maps: Current, Historic, KDOT
 Kansas Railroad Maps: Current, 1996, 1915, KDOT and Kansas Historical Society

 
Kansas counties
1867 establishments in Kansas
Populated places established in 1867



ground_truth: 30,223.