# Summarizes accelerometer data for a single type of physical activity Summarizes accelerometer data for a single type of physical activity. Functionality is same as function acc, except that this function provides a summary for a single type of physical activity, with more detailed information. ```r accSummary(data, tri, axis, spuriousDef, nonwearDef, minWear, patype, pacut, epoch, boutsize, tolerance, returnbout) ``` ## Arguments - `data`: Data which consists of two columns [TimeStamp,counts] (i.e. raw accelerometer file read in by function readRaw) - `tri`: Whether the data is from a tri-axial accelerometer. Default is tri=`FALSE'. If tri=`TRUE' then option `axis' should be specified. - `axis`: This option is only used for the tri-axial accelerometer. Options are `x',`y',`z',`sum', or `vm'. Options `x', `y', or `z' can be spefied to summarize data using only data from a single axis. If the option 'vm' is used, the square root of the squared sum of counts from three axes (i.e. $\sqrt{{x}^{2}+{y}^{2}+{z}^{2}}$ are used for the summary. If the option 'sum' is used, sum of the counts from three axes are used. - `spuriousDef`: Definition of spurious observation. Defined as minutes of consecutive zeros. For example, if spuriousDef = 20, this means that an observation point will be determined as a spurious observation if there are consequtive counts of at least 20 zeros before and after the single non-zero observation. Default is spuriousDef = 20. - `nonwearDef`: Definition of non-wear time. Defined as minutes of consecutive zeros. For example, if nonwearDef=60, this means that a period will be defined as non-wear time if there are at least 60 consecutive zeros. Default is nonwearDef=60. To consider all observations as wear time specify nonwearDef=`Inf' - `minWear`: Minimum wear time definition. Defined as minutes of wear time. or example, if minWear = 600, this means that a day will be considered valid only if the wear time is at least 600 minutes. Default is minWear = 600. To return summary for all dates in the data, set minWear = 0. - `patype`: Type of physical activity for summary. For example, to summarize sedentary activity, use option patype=c(`Sedentary'). To summarize moderate-vigorous physical activities, user specifies patype=c(`MVPA'). This labels the summary accordingly. - `pacut`: Cut points to be used for the physical activity type. For example, if the user specified patype=c(`Sedentary'), pacut can be specified as pacut=c(c(0,99)). The options requires to have a lower and a upper limit for each activity type (i.e. c(0,99) for sedentary activity). The specified interval includes its lower and upper endpoints (it is a closed inerval). - `boutsize`: Boutsize to summarize a physical activity. If multiple patype is specified, boutsize should be for each one (e.g., if patype=c(`Sedentary') then one can use boutsize=c(10)). - `epoch`: Epoch size. Default is '1 min'. Other epoch size can be specified using this option (e.g., '1 sec') - `tolerance`: Whether two observations outside the physical activity cut point should be permitted in summarizing a physical activity (e.g. if patype=c(`Sedentary') then one can use tolerance=c(`FALSE')). - `returnbout`: Whether to return data with bout indicators. If returnbout='FALSE' then only a summary of daily physical activity is returned. ## Returns If returnbout='FALSE', then a summary for each specified physical activity types (number of bouts and minutes of the activity), for valid dates. Defaults to 'TRUE'. If returnbout='TRUE', then a list of summary object is returned with: - **totalDates**: Number of unique days available in data. - **validDates**: A summary for each specified physical activity types (number of bouts and minutes of the activity), for valid dates (as defined by minWear). - **PA**: Data supplied by the user is returned with three additional columns [inPA, nonwear, inboutPA], indicating whether the observation was considered to be in the defined cutpoint of the physical activity (inPA), in the defined cutpoint of nonwear time (nonwear), or in bout (inboutPA), respectively. ## Author(s) Jaejoon Song ## References Choi, L., Liu, Z., Matthews, C.E. and Buchowski, M.S. (2011). Validation of Accelerometer Wear and Nonwear Time Classification Algorithm. Med Sci Sports Exerc, 43(2):357-64. Hall, K. S., Howe, C. A., Rana, S. R., Martin, C. L., and Morey, M. C. (2013). METs and Accelerometry of Walking in Older Adults: Standard versus Measured Energy Cost. Medicine and Science in Sports and Medicine, 45(3). 574-82. Freedson, P., Melanson, E., and Sirard, J. (1998). Calibration of the Computer Sciences and Applications, Inc. accelerometer. Medicine and Science in Sports and Exercercise, 30(5):777-81. Swartz, A. M., Strath, S. J., Bassett, D. R. Jr., O'Brien, W. L., King, G. A., and Ainsworth, B. E. (2000). Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. Medicine and Science in Sports and Exercercise, 32: S450-456. Copeland, J. L., and Esliger, D. W. (2009). Accelerometer assessment of physical activity in active, healthy older adults. J Aging Phys Act, 17: 17-30. ## Examples ```r ## ## Example 1: Loading the activity counts data using readCounts function ## ## Not run: library(acc) infile <- "DataName.dat" counts <- readCounts(infile) ## ## Example 2: Summarizing accelerometer data for a sedentary individual" ## # For this example, data is generated using a Hidden Markov model # First, a sequence of time is generated randomTime <- seq(ISOdate(2015,4,1),ISOdate(2015,4,3),"min") # Load the mhsmm package to generate data using a Hidden Makov model library(mhsmm) # It is assumed that the counts are generated from a Hidden Markov model # with three states, being non-wear, sedentary, and moderate-vigorous activity J <- 3; initial <- rep(1/J, J) # Set up a transition matrix for the Hidden Markov model. P <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.9, 0.01, 0.1, 0.2, 0.7), byrow='TRUE',nrow = J) # It is assumed that the counts are realized from a mixture of # two normal distributions (for sedentary activity and mvpa) # and a constant at zero (for non-wear time). b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000)) model <- hmmspec(init = initial, trans = P, parms.emission = b,dens.emission = dnorm.hsmm) # Generate data! train <- simulate.hmmspec(model, nsim = (60*24*2), seed = 1234, rand.emis = rnorm.hsmm) # Now set up a dataset that mimicks the accelerometry data counts <- data.frame(TimeStamp = randomTime[1:length(train$x)], counts = train$x) library(acc) # summarize the data using the acc function. # Sedentary and moderate-vigorous activity is summarized, using Freedson's cut points by default. # Option returnbout='TRUE' returns a more detailed information on how the summary was calculated. summary1 <- accSummary(data=counts, tri='FALSE', axis=NULL, spuriousDef=20, nonwearDef=60, minWear=600, patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE',returnbout='TRUE') summary1$validDates # This returns the same summary as when returnbout='FALSE' # summary1$PA # This returns the activity classification and bout information ## ## Example 3: Summarizing accelerometer data for an active individual. ## randomTime <- seq(ISOdate(2015,4,1),ISOdate(2015,4,3),"min") library(mhsmm) J <- 3; initial <- rep(1/J, J) P <- matrix(c(0.95, 0.04, 0.01, 0.09, 0.7, 0.21, 0.1, 0.1, 0.8), byrow='TRUE',nrow = J) b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000)) model <- hmmspec(init = initial, trans = P, parms.emission = b,dens.emission = dnorm.hsmm) train <- simulate.hmmspec(model, nsim = (60*24*2), seed = 1234, rand.emission = rnorm.hsmm) counts <- data.frame(TimeStamp = randomTime[1:length(train$x)], counts = train$x) library(acc) # Option returnbout='TRUE' returns a more detailed information on how the summary was calculated. summary2 <- accSummary(data=counts, tri='FALSE', axis=NULL, spuriousDef=20, nonwearDef=60, minWear=600, patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE',returnbout='TRUE') summary2$validDates # This returns the same summary as when returnbout='FALSE' # summary2$PA # This returns the activity classification and bout information ## End(Not run) ```

accSummary function