Focus on Methods
Sensor-Based Detection of Gait Speed in Older Adults An Integrative Review Blaine Reeder, PhD; and Kamin Whitehouse, PhD, MS
ABSTRACT
Gait speed is an indicator of functional change in older adults. One approach to support older adults’ preferences to “age in place” is through the use of technology to monitor gait speed in everyday life. The authors of the current article conducted an integrative review of the scientific literature to summarize the current state of gait speed detection technologies. A total of 539 articles were returned from searches, and 16 were included in the review. Technologies were categorized as body-worn or home-installed sensors. Evidence was classified as emerging (n = 8) or promising (n = 8). Gait speed technology research has advanced beyond the use of customized research hardware prototypes, and consumer technologies are now commonly used in gait speed research. However, a need exists for software systems that integrate data for analysis and presentation to stakeholders with different information needs. Future research should focus on approaches to integrate disparate data sources and visualizations of gait speed data. [Res Gerontol Nurs. 2015; 8(1):12-27.]
Gait speed—proposed as the “sixth” vital sign (Fritz & Lusardi, 2009)—is an important indicator of health outcomes and functional change in older adults. Slow gait speed is associated with mobility limitations (MonteroOdasso et al., 2005); falls (Montero-Odasso et al., 2005; Verghese, Holtzer, Lipton, & Wang, 2009); nursing home placement (Verghese et al., 2009); hospitalization (Montero-Odasso et al., 2005); and mortality (MonteroOdasso et al., 2005) in older adults. Gait speed alone predicts disability in previously healthy community-dwelling older adults (Guralnik et al., 2000). Specifically, variability
in gait speed is associated with frailty in communitydwelling older adults (Montero-Odasso et al., 2011) and is also an indicator of independence in performing activities of daily living (Potter, Evans, & Duncan, 1995). Improvement in usual gait speed predicts reductions in mortality and improved survival in older adults (Hardy, Perera, Roumani, Chandler, & Studenski, 2007). The population of older adults in the United States is projected to grow to more than 81 million and constitute 20% of the total U.S. population by 2040 (Federal Interagency Forum on Aging-Related Statistics, 2012). This
Dr. Reeder is Assistant Professor, College of Nursing, University of Colorado, Anschutz Medical Campus, Aurora, Colorado; and Dr. Whitehouse is Associate Professor, Computer Science Department, University of Virginia, Charlottesville, Virginia. The authors have disclosed no potential conflicts of interest, financial or otherwise. Address correspondence to Blaine Reeder, PhD, Assistant Professor, College of Nursing, University of Colorado, Anschutz Medical Campus, Mail Stop C288-19, 13120 E. 19th Avenue, Ed2 North, Aurora, CO 80045; e-mail: [email protected]. Received: May 9, 2014; Accepted: August 20, 2014; Posted: December 1, 2014 doi:10.3928/19404921-20141120-02
12
Copyright © SLACK Incorporated
Detection of Gait Speed in Older Adults
demographic shift will increase demand for aging services in conjunction with a shortage of gerontological health care workers (Cohen, 2009). Therefore, a critical need exists for cost-effective, informatics-based solutions that support community-based interventions that can scale to meet the needs of an aging population (Institute of Medicine, 2012). A majority of older adults desire to live independently in their communities as they grow older (i.e., “age in place”) (Farber, Shinkle, Lynott, Fox-Grage, & Harrell, 2011). A potential approach to support this desire is through the use of technology to monitor gait speed in everyday life as an indicator of functional decline. Such an indicator could be used to enhance existing home-based interventions for community-dwelling older adults at risk for functional decline, such as Advancing Better Living for Elders (Gitlin et al., 2006; Gitlin, Earland, Piersol, & Shaw, 2010) and Community Aging in Place Advancing Better Living for Elders (Szanton et al., 2011), or physical activity interventions that increase strength and mobility to prevent hospital readmissions for homebound older adults (Fisher et al., 2013). Therefore, the aim of the current study was to provide an integrative review of sensor-based gait speed detection technologies by identifying strengths and limitations of different approaches from studies published in the scientific literature.
METHOD Search Strategy The authors of the current article used Whittemore and Knafl’s (2005) methodology to guide the integrative review process. To provide interdisciplinary coverage of the published literature, searches of PubMed and IEEE Xplore databases were conducted by the first author (B.R.) in November 2013. Searches were conducted using keyword combinations of the following MeSH and non-MeSH terms tailored to the review topic: walking speed, walk speed, gait speed, gait analysis, gait recognition, fall risk, mobility or motor activity plus technology, sensor or system plus aged, elderly, older adult plus house, home, residence plus monitor, monitoring, and assessment. In addition, the authors reviewed the reference lists of seven review articles (Culhane, O’Connor, Lyons, & Lyons, 2005; de Bruin, Hartmann, Uebelhart, Murer, & Zijlstra, 2008; Krenn, Titze, Oja, Jones, & Ogilvie, 2011; Ni Scanaill et al., 2006; Schwenk et al., 2014; Taraldsen, Chastin, Riphagen, Vereijken, & Helbostad, 2012; Terrier & Schutz, 2005) and candidate articles that met inclusion criteria during full-text review to identify possible articles for in-
Research in Gerontological Nursing • Vol. 8, No. 1, 2015
clusion. Reference list review occurred from December 2013 to March 2014. Studies that met inclusion criteria (a) described gait speed detection technologies tested with older adult participants and (b) met quality-of-reporting criteria during data evaluation. Studies that were excluded described computational methods, system overviews, technical descriptions, and proposed study protocols. Data evaluation for quality of reporting was conducted as part of the screening process for article inclusion using an approach from a systematic review of fall detection devices (Chaudhuri, Thompson, & Demiris, 2014). Data evaluation involved scoring each article using six items from the STARE-HI guidelines for reporting informatics studies (de Keizer et al., 2010, 2012; Talmon et al., 2009). These items are (a) description of technology function; (b) study type (i.e., laboratory, simulation, or field study); (c) description of outcome measures/evaluation criteria; (d) participant demographics; (e) basic numbers for study results; and (f) interpretation of data (Chaudhuri et al., 2014). Articles that did not report all six items were excluded. In addition, the authors excluded articles that were duplicative reports of the same study. In total, 539 articles were returned from searches and reference list reviews. During abstract review, the authors identified 75 articles for full-text review. The first author downloaded and reviewed the full text of each of these 75 candidate articles. An additional 56 articles were excluded during this process, leaving 19 articles for inclusion. One additional article was identified from the review of reference lists of candidate articles during full-text review, increasing the total to 20 articles. Four articles were excluded during data evaluation (Alwan, 2009; Gietzelt, Wolf, Kohlmann, Marschollek, & Haux, 2013; LopezMeyer, Fulk, & Sazonov, 2011; Rantz et al., 2013), thus decreasing the total to 16 articles included for abstraction. Data Analysis The first author reviewed each included article and abstracted the following information: (a) study design, (b) sample size, (c) technology type, (d) technical description, (e) acceptability, and (f) strengths and limitations. Many studies reported multiple gait parameters. For the purposes of the current review, the authors focused on gait speed. The second author (K.W.) reviewed the list of included articles for completeness and potentially missing studies based on research expertise with sensing technologies. Studies were classified according to the concepts of emerging, promising, effective (first tier), and effective
13
Reeder & Whitehouse
TABLE 1
Article Publication Type and Country of Origin Publication Type
Author(s)
Journal (n = 11)
Aminian et al., 2002; Dadashi et al., 2013; Dobkin et al., 2011; Hayes et al., 2008; Kaye et al., 2011; Mariani et al., 2010; Menz et al., 2003b; Theou et al., 2012; Webber & Porter, 2009; Wettstein et al., 2012; Zijlstra, 2004
Conference (n = 5)
Cuddihy et al., 2012; Hagler et al., 2010; Stone & Skubic, 2013; Wang et al., 2009; Wang et al., 2013
Country United States (n = 8)
Cuddihy et al., 2012; Dobkin et al., 2011; Hagler et al., 2010; Hayes et al., 2008; Kaye et al., 2011; Stone & Skubic, 2013; Wang et al., 2009; Wang et al., 2013
Switzerland (n = 3)
Aminian et al., 2002; Dadashi et al., 2013; Mariani et al., 2010
Australia (n = 1)
Menz et al., 2003b
Canada (n = 1)
Webber & Porter, 2009
Greece (n = 1)
Theou et al., 2012
Israel/Germany (n = 1)
Wettstein et al., 2012
Netherlands (n = 1)
Zijlstra, 2004
(second tier) categories from an evidence-based public health typology (Brownson, Baker, Leet, Gillespie, & True, 2011; Brownson, Fielding, & Maylahn, 2009), as operationalized in a systematic review of smart-home technologies (Reeder, Meyer, et al., 2013) and as adapted for the current review. Emerging technology studies enroll at least one older adult participant, typically make statements about technology improvement based on study results, are often technology trials, and test technology function or user effect in laboratory or field settings. Promising technology studies enroll 10 or more older adult participants in a natural setting (i.e., field or “living laboratory” setting), describe technology strengths and weaknesses, involve well-tested technologies that are beyond the early prototype stage, and test technology function and user effect. Effective (first tier) and effective (second tier) categories have all the characteristics of emerging and promising evidence and show positive changes in health outcomes for participants who receive an intervention supported by gait speed technology. Effective (first tier) evidence is demonstrated in a single setting, whereas effective (second tier) evidence is demonstrated in two or more settings. The authors’ aim was to present a concise overview of the most advanced stage of development for each gait speed technology type. Therefore, although promising evidence existed from a progression of studies from the same research group, the
14
authors did not abstract emerging evidence where such reporting would have been redundant. Instead, the authors provide citations to these articles.
RESULTS Included Articles Sixteen articles published between 2002 and 2014 were included for abstraction; none of the included articles was published between 2005 and 2007. Included articles were predominantly published in journals (n = 11) versus presented at conferences (n = 5) and originated from eight different countries. Table 1 shows all included articles by publication type and country of origin. Technology evidence was classified as either emerging (n = 8) or promising (n = 8); no articles reported effective (first tier) or effective (second tier) evidence. Technologies were closely divided between categories of bodyworn (n = 9) and home-installed sensors (n = 7). Studies of body-worn sensors were of the following technology types: (a) accelerometer only (n = 3), (b) global positioning system (GPS) only (n = 1), (c) GPS and accelerometer combinations (n = 2), and (d) shoe sensors (n = 3, i.e., gyroscopes). (The authors grouped three studies as shoe sensors because all three studies came from the same research group and used the same ergonomic approach despite the use of different technology types.) Studies of home-installed sensors
Copyright © SLACK Incorporated
Research in Gerontological Nursing • Vol. 8, No. 1, 2015 Descriptive
Dadashi et al., 2013
Descriptive
Wang et al., 2009
Wang et al., 2013 Feasibility Descriptive Longitudinal cohort
Hayes et al., 2008
Hagler et al., 2010
Kaye et al., 2011
265
27
14
10
10
15
13
1,433
20
20
50
20
257
18
41
60
Sample (N)
Note. USD = U.S. dollars; GPS = global positioning system. a Costs are not reported for radically customized research prototypes because these technologies are unavailable for purchase. b Manufacturer and model were not reported; no cost information is available. c Two sensors and analytic software. d This product is no longer available.
Motion sensors
Descriptive
Stone & Skubic, 2013
Microsoft® Kinect®
Web camera
Descriptive
Cuddihy et al., 2012
Descriptive
Descriptive
Mariani et al., 2010
Doppler radar
Home-installed
Descriptive
Aminian et al., 2002
Descriptive
Theou et al., 2012
Shoe sensor
Feasibility
Webber & Porter, 2009
GPS and accelerometer
Descriptive
Dobkin et al., 2011 Descriptive
Descriptive
Zijlstra, 2004
Wettstein et al., 2012
Descriptive
Study Design
Menz et al., 2003b
Author(s)
GPS
Accelerometer
Body-worn
Technology Category and Type
Field
Laboratory
Field
Laboratory
Laboratory
Field
Laboratory
Laboratory
Laboratory
Laboratory
Field
Field
Field
Laboratory
Laboratory
Laboratory
Study Type
Promising
Promising
Promising
Emerging
Emerging
Promising
Emerging
Promising
Emerging
Emerging
Promising
Promising
Promising
Emerging
Emerging
Emerging
Evidence
Consumer sensors
Consumer sensors
Consumer sensors
Consumer camera
Consumer camera
Consumer console
Consumer radar
Commercial research
Research prototype
Research prototype
Research accelerometer
Consumer watch
Research accelerometer
Consumer watch
Consumer watch
Consumer accelerometer
Research prototype
Research prototype
Audience/Maturity
Gait Speed Detection Technology Studies by Technology Category and Type
TABLE 2
Sensor-Based Detection of Gait Speed in Older Adults An Integrative Review Blaine Reeder, PhD; and Kamin Whitehouse, PhD, MS
ABSTRACT
Gait speed is an indicator of functional change in older adults. One approach to support older adults’ preferences to “age in place” is through the use of technology to monitor gait speed in everyday life. The authors of the current article conducted an integrative review of the scientific literature to summarize the current state of gait speed detection technologies. A total of 539 articles were returned from searches, and 16 were included in the review. Technologies were categorized as body-worn or home-installed sensors. Evidence was classified as emerging (n = 8) or promising (n = 8). Gait speed technology research has advanced beyond the use of customized research hardware prototypes, and consumer technologies are now commonly used in gait speed research. However, a need exists for software systems that integrate data for analysis and presentation to stakeholders with different information needs. Future research should focus on approaches to integrate disparate data sources and visualizations of gait speed data. [Res Gerontol Nurs. 2015; 8(1):12-27.]
Gait speed—proposed as the “sixth” vital sign (Fritz & Lusardi, 2009)—is an important indicator of health outcomes and functional change in older adults. Slow gait speed is associated with mobility limitations (MonteroOdasso et al., 2005); falls (Montero-Odasso et al., 2005; Verghese, Holtzer, Lipton, & Wang, 2009); nursing home placement (Verghese et al., 2009); hospitalization (Montero-Odasso et al., 2005); and mortality (MonteroOdasso et al., 2005) in older adults. Gait speed alone predicts disability in previously healthy community-dwelling older adults (Guralnik et al., 2000). Specifically, variability
in gait speed is associated with frailty in communitydwelling older adults (Montero-Odasso et al., 2011) and is also an indicator of independence in performing activities of daily living (Potter, Evans, & Duncan, 1995). Improvement in usual gait speed predicts reductions in mortality and improved survival in older adults (Hardy, Perera, Roumani, Chandler, & Studenski, 2007). The population of older adults in the United States is projected to grow to more than 81 million and constitute 20% of the total U.S. population by 2040 (Federal Interagency Forum on Aging-Related Statistics, 2012). This
Dr. Reeder is Assistant Professor, College of Nursing, University of Colorado, Anschutz Medical Campus, Aurora, Colorado; and Dr. Whitehouse is Associate Professor, Computer Science Department, University of Virginia, Charlottesville, Virginia. The authors have disclosed no potential conflicts of interest, financial or otherwise. Address correspondence to Blaine Reeder, PhD, Assistant Professor, College of Nursing, University of Colorado, Anschutz Medical Campus, Mail Stop C288-19, 13120 E. 19th Avenue, Ed2 North, Aurora, CO 80045; e-mail: [email protected]. Received: May 9, 2014; Accepted: August 20, 2014; Posted: December 1, 2014 doi:10.3928/19404921-20141120-02
12
Copyright © SLACK Incorporated
Detection of Gait Speed in Older Adults
demographic shift will increase demand for aging services in conjunction with a shortage of gerontological health care workers (Cohen, 2009). Therefore, a critical need exists for cost-effective, informatics-based solutions that support community-based interventions that can scale to meet the needs of an aging population (Institute of Medicine, 2012). A majority of older adults desire to live independently in their communities as they grow older (i.e., “age in place”) (Farber, Shinkle, Lynott, Fox-Grage, & Harrell, 2011). A potential approach to support this desire is through the use of technology to monitor gait speed in everyday life as an indicator of functional decline. Such an indicator could be used to enhance existing home-based interventions for community-dwelling older adults at risk for functional decline, such as Advancing Better Living for Elders (Gitlin et al., 2006; Gitlin, Earland, Piersol, & Shaw, 2010) and Community Aging in Place Advancing Better Living for Elders (Szanton et al., 2011), or physical activity interventions that increase strength and mobility to prevent hospital readmissions for homebound older adults (Fisher et al., 2013). Therefore, the aim of the current study was to provide an integrative review of sensor-based gait speed detection technologies by identifying strengths and limitations of different approaches from studies published in the scientific literature.
METHOD Search Strategy The authors of the current article used Whittemore and Knafl’s (2005) methodology to guide the integrative review process. To provide interdisciplinary coverage of the published literature, searches of PubMed and IEEE Xplore databases were conducted by the first author (B.R.) in November 2013. Searches were conducted using keyword combinations of the following MeSH and non-MeSH terms tailored to the review topic: walking speed, walk speed, gait speed, gait analysis, gait recognition, fall risk, mobility or motor activity plus technology, sensor or system plus aged, elderly, older adult plus house, home, residence plus monitor, monitoring, and assessment. In addition, the authors reviewed the reference lists of seven review articles (Culhane, O’Connor, Lyons, & Lyons, 2005; de Bruin, Hartmann, Uebelhart, Murer, & Zijlstra, 2008; Krenn, Titze, Oja, Jones, & Ogilvie, 2011; Ni Scanaill et al., 2006; Schwenk et al., 2014; Taraldsen, Chastin, Riphagen, Vereijken, & Helbostad, 2012; Terrier & Schutz, 2005) and candidate articles that met inclusion criteria during full-text review to identify possible articles for in-
Research in Gerontological Nursing • Vol. 8, No. 1, 2015
clusion. Reference list review occurred from December 2013 to March 2014. Studies that met inclusion criteria (a) described gait speed detection technologies tested with older adult participants and (b) met quality-of-reporting criteria during data evaluation. Studies that were excluded described computational methods, system overviews, technical descriptions, and proposed study protocols. Data evaluation for quality of reporting was conducted as part of the screening process for article inclusion using an approach from a systematic review of fall detection devices (Chaudhuri, Thompson, & Demiris, 2014). Data evaluation involved scoring each article using six items from the STARE-HI guidelines for reporting informatics studies (de Keizer et al., 2010, 2012; Talmon et al., 2009). These items are (a) description of technology function; (b) study type (i.e., laboratory, simulation, or field study); (c) description of outcome measures/evaluation criteria; (d) participant demographics; (e) basic numbers for study results; and (f) interpretation of data (Chaudhuri et al., 2014). Articles that did not report all six items were excluded. In addition, the authors excluded articles that were duplicative reports of the same study. In total, 539 articles were returned from searches and reference list reviews. During abstract review, the authors identified 75 articles for full-text review. The first author downloaded and reviewed the full text of each of these 75 candidate articles. An additional 56 articles were excluded during this process, leaving 19 articles for inclusion. One additional article was identified from the review of reference lists of candidate articles during full-text review, increasing the total to 20 articles. Four articles were excluded during data evaluation (Alwan, 2009; Gietzelt, Wolf, Kohlmann, Marschollek, & Haux, 2013; LopezMeyer, Fulk, & Sazonov, 2011; Rantz et al., 2013), thus decreasing the total to 16 articles included for abstraction. Data Analysis The first author reviewed each included article and abstracted the following information: (a) study design, (b) sample size, (c) technology type, (d) technical description, (e) acceptability, and (f) strengths and limitations. Many studies reported multiple gait parameters. For the purposes of the current review, the authors focused on gait speed. The second author (K.W.) reviewed the list of included articles for completeness and potentially missing studies based on research expertise with sensing technologies. Studies were classified according to the concepts of emerging, promising, effective (first tier), and effective
13
Reeder & Whitehouse
TABLE 1
Article Publication Type and Country of Origin Publication Type
Author(s)
Journal (n = 11)
Aminian et al., 2002; Dadashi et al., 2013; Dobkin et al., 2011; Hayes et al., 2008; Kaye et al., 2011; Mariani et al., 2010; Menz et al., 2003b; Theou et al., 2012; Webber & Porter, 2009; Wettstein et al., 2012; Zijlstra, 2004
Conference (n = 5)
Cuddihy et al., 2012; Hagler et al., 2010; Stone & Skubic, 2013; Wang et al., 2009; Wang et al., 2013
Country United States (n = 8)
Cuddihy et al., 2012; Dobkin et al., 2011; Hagler et al., 2010; Hayes et al., 2008; Kaye et al., 2011; Stone & Skubic, 2013; Wang et al., 2009; Wang et al., 2013
Switzerland (n = 3)
Aminian et al., 2002; Dadashi et al., 2013; Mariani et al., 2010
Australia (n = 1)
Menz et al., 2003b
Canada (n = 1)
Webber & Porter, 2009
Greece (n = 1)
Theou et al., 2012
Israel/Germany (n = 1)
Wettstein et al., 2012
Netherlands (n = 1)
Zijlstra, 2004
(second tier) categories from an evidence-based public health typology (Brownson, Baker, Leet, Gillespie, & True, 2011; Brownson, Fielding, & Maylahn, 2009), as operationalized in a systematic review of smart-home technologies (Reeder, Meyer, et al., 2013) and as adapted for the current review. Emerging technology studies enroll at least one older adult participant, typically make statements about technology improvement based on study results, are often technology trials, and test technology function or user effect in laboratory or field settings. Promising technology studies enroll 10 or more older adult participants in a natural setting (i.e., field or “living laboratory” setting), describe technology strengths and weaknesses, involve well-tested technologies that are beyond the early prototype stage, and test technology function and user effect. Effective (first tier) and effective (second tier) categories have all the characteristics of emerging and promising evidence and show positive changes in health outcomes for participants who receive an intervention supported by gait speed technology. Effective (first tier) evidence is demonstrated in a single setting, whereas effective (second tier) evidence is demonstrated in two or more settings. The authors’ aim was to present a concise overview of the most advanced stage of development for each gait speed technology type. Therefore, although promising evidence existed from a progression of studies from the same research group, the
14
authors did not abstract emerging evidence where such reporting would have been redundant. Instead, the authors provide citations to these articles.
RESULTS Included Articles Sixteen articles published between 2002 and 2014 were included for abstraction; none of the included articles was published between 2005 and 2007. Included articles were predominantly published in journals (n = 11) versus presented at conferences (n = 5) and originated from eight different countries. Table 1 shows all included articles by publication type and country of origin. Technology evidence was classified as either emerging (n = 8) or promising (n = 8); no articles reported effective (first tier) or effective (second tier) evidence. Technologies were closely divided between categories of bodyworn (n = 9) and home-installed sensors (n = 7). Studies of body-worn sensors were of the following technology types: (a) accelerometer only (n = 3), (b) global positioning system (GPS) only (n = 1), (c) GPS and accelerometer combinations (n = 2), and (d) shoe sensors (n = 3, i.e., gyroscopes). (The authors grouped three studies as shoe sensors because all three studies came from the same research group and used the same ergonomic approach despite the use of different technology types.) Studies of home-installed sensors
Copyright © SLACK Incorporated
Research in Gerontological Nursing • Vol. 8, No. 1, 2015 Descriptive
Dadashi et al., 2013
Descriptive
Wang et al., 2009
Wang et al., 2013 Feasibility Descriptive Longitudinal cohort
Hayes et al., 2008
Hagler et al., 2010
Kaye et al., 2011
265
27
14
10
10
15
13
1,433
20
20
50
20
257
18
41
60
Sample (N)
Note. USD = U.S. dollars; GPS = global positioning system. a Costs are not reported for radically customized research prototypes because these technologies are unavailable for purchase. b Manufacturer and model were not reported; no cost information is available. c Two sensors and analytic software. d This product is no longer available.
Motion sensors
Descriptive
Stone & Skubic, 2013
Microsoft® Kinect®
Web camera
Descriptive
Cuddihy et al., 2012
Descriptive
Descriptive
Mariani et al., 2010
Doppler radar
Home-installed
Descriptive
Aminian et al., 2002
Descriptive
Theou et al., 2012
Shoe sensor
Feasibility
Webber & Porter, 2009
GPS and accelerometer
Descriptive
Dobkin et al., 2011 Descriptive
Descriptive
Zijlstra, 2004
Wettstein et al., 2012
Descriptive
Study Design
Menz et al., 2003b
Author(s)
GPS
Accelerometer
Body-worn
Technology Category and Type
Field
Laboratory
Field
Laboratory
Laboratory
Field
Laboratory
Laboratory
Laboratory
Laboratory
Field
Field
Field
Laboratory
Laboratory
Laboratory
Study Type
Promising
Promising
Promising
Emerging
Emerging
Promising
Emerging
Promising
Emerging
Emerging
Promising
Promising
Promising
Emerging
Emerging
Emerging
Evidence
Consumer sensors
Consumer sensors
Consumer sensors
Consumer camera
Consumer camera
Consumer console
Consumer radar
Commercial research
Research prototype
Research prototype
Research accelerometer
Consumer watch
Research accelerometer
Consumer watch
Consumer watch
Consumer accelerometer
Research prototype
Research prototype
Audience/Maturity
Gait Speed Detection Technology Studies by Technology Category and Type
TABLE 2
