Advanced Sensing and Human Activity Recognition in Early Intervention and Rehabilitation of Elderly People

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

In the first stage, data from a total of five healthy persons were recorded: four in the laboratory environment and one in the hospital environment, with ages ranging from 24 to 55 years.

“In this first stage, data from a total of five healthy persons were recorded.There were recorded data of four persons in the laboratory environment and of one additional participant in the hospital environment.Their age was in the range of 24 to 55 years.”

Classified as probably not a substantial claim.

The data acquisition of several sessions one after the other is physically demanding.

“Although the recorded activities are part of daily life, the data acquisition of several sessions one after the other is physically demanding.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The prototypic SmartCardia wearable could not be used due to a missing CE certificate.

“The only change of technical equipment concerned the prototypic SmartCardia wearable, which could not be used due to a missing CE certificate.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The most time-consuming task in data acquisition was storing the data.

“It also demonstrated that the most time-consuming task was storing the data.”

May combine two claims.

Overall, 15 participants were recruited: eight elderly persons and seven neurological patients.

“Overall, we recruited a group of 15 participants consisting of eight elderly persons and seven neurological patients.”

• Also stated in: § Results · plain

  “During this trial, eight healthy elderly persons and seven neurological patients participated.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The 5XSST could be performed only by 4 patients as 3 patients were not able to stand up without an assistive device.

“The 5XSST could be performed only by 4 patients as 3 patients were not able to stand up without an assistive device.”

Probably not a substantial claim: may not pass S3 (load-bearing).

All patients were in rehabilitation phase B.

“All patients were in rehabilitation phase B.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The neurological patient group consisted of 5 men and 2 women aged 30 to 84 years with a mean age of 64.0 years (SD 17.5).

“The group consisted of 5 men and 2 woman aged 30 to 84 years with a mean age of 64.0 years (SD 17.5).”

May combine two claims.

The selected group of neurological patients represented different levels of motor and cognitive function: two patients after ischaemic stroke, two with Parkinson's Disease, one with Guillain-Barré-Syndrome, one with Alzheimer's Disease and one with Multiple Sclerosis.

“The selected group of neurological patients represented different levels of motor and cognitive function: two patients after ischaemic stroke, two with Parkinson's Disease, one with Guillain-Barré-Syndrome, one with Alzheimer's Disease and one with Multiple Sclerosis.”

The quote only partly states this claim.

The hand grip force for patients ranged from 8.9 to 36.5 kg with a mean of 16.7 kg (SD 9.8).

“The hand grip force ranged from 8.9 to 36.5 kg with a mean of 16.7 kg (SD 9.8).”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The group of healthy elderly people consisted of 5 men and 3 women aged 67 to 86 years with a mean age of 74.4 years (SD 6.5).

“The group of healthy elderly people consisted of 5 men and 3 women aged 67 to 86 years with a mean age of 74.4 years (SD 6.5).”

Probably not a substantial claim: may not pass S3 (load-bearing).

The generation of labeled training data is a very time-consuming task.

“On the other hand, the generation of labeled training data also becomes a very time-consuming task.”

Probably not a substantial claim: may not pass S3 (load-bearing).

So far, a selection of mid-level activities has been annotated: washing hands, brushing teeth, toileting, pull up/down the pants, eating, drinking, pill taking and the corresponding hand gestures for the last two.

“So far we have annotated a selection of mid-level activities, which are washing hands, brushing teeth, toileting, pull up/down the pants, eating, drinking, pill taking and the corresponding hand gestures for the last two.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The annotation process of the HAR database is currently in progress.

“The annotation process of the HAR database is currently in progress.”

May combine two claims.

Enthusiasm for technology resulted to be the lowest dimension, but the participants' ratings showed the highest value in positive attitude towards technology for each individual.

“Enthusiasm for technology resulted to be the lowest dimension, but nevertheless the participants' ratings showed the highest value in positive attitude towards technology for each individual.”

May combine two claims.

Although all healthy elderly persons were highly interested and motivated in participating in a study aiming at a monitoring or intervention system, their overall general technical affinity was only moderate with a mean value of 3.0 points (SD 0.6).

“Although all healthy elderly persons were highly interested and motivated in participating in a study aiming at a monitoring or intervention system, their overall general technical affinity was only moderate with a mean value of 3.0 points (SD 0.6).”

May combine two claims.

Ageing is associated with a decline in physical activity and a decrease in the ability to perform activities of daily living, affecting physical and mental health.

“This increase has proved to be a major challenge to the healthcare system, since ageing is associated with a decline in physical activity, affecting physical and mental health.Strategies are needed to enable older people to continue their daily living, prevent diseases or support in rehabilitation, which is costly to individuals and the healthcare system (WHO 2002).”

• cites WHO 2002Not checked yet

  Not checked in the free run.

Classified as probably not a substantial claim.

A HAR system can support elderly or diseased people by detecting their activity patterns and intervening in case of changes in behaviour or a critical event.

“A HAR system based on an ARC can then be used to support elderly or diseased people by detecting their activity patterns and intervening in case of changes in behaviour or a critical event (e.g. by sending recommendations or information to the person affected).”

May combine two claims.

Perfect segmentation of activities is difficult because humans perform activities in a fluent way and segmentation of time series into intervals representing single activities is not straightforward.

“While the HAR problem seeks to find a perfect segmentation of activities, this task is difficult to achieve since humans perform activities in a fluent way and the segmentation of time series into intervals that represent single activities is not straightforward (Bulling et al. 2014).”

• cites Bulling et al. 2014 sourceNot checked yet

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May combine two claims.

Data collection for HAR is challenging and time-consuming and involves general challenges on data collection, synchronization and multi-modal sensor arrangements.

“The data collection for HAR is a challenging and time consuming task that involves general challenges on data collection, synchronization and multi-modal sensor arrangements (Calatroni et al. 2011).”

• cites Calatroni et al. 2011 sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based).

A HAR system can enable elderly or diseased people to have a more independent life.

“A HAR system could enable these people to have a more independent life.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Most research in HAR follows a specific sequence of processing principles called the activity recognition chain (ARC).

“Most research in the HAR context follows a specific sequence of processing principles to interpret human activities from sensor data, called activity recognition chain (ARC) (Roggen et al. 2010).”

• cites Roggen et al. 2010 sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Machine learning algorithms are widely used for the development of HAR systems to recognize patterns of human activity in sensor data.

“Therefore, machine learning algorithms are widely used for the development of HAR systems in order to recognize patterns of human activity in the sensor data.”

May combine two claims.

Human activity is highly diverse and recognition of specific activities requires accurate selection of sensors and their placement.

“The second aspect that goes along with this is that human activity is highly diverse and the recognition of specific activities therefore requires an accurate selection of sensors and their placement (Bulling et al. 2014).”

• cites Bulling et al. 2014 sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Most work in HAR divides measurements into fixed intervals and recognizes the activity that took place within each interval, known as the relaxed HAR problem.

“Therefore, most of the work in HAR is dedicated to dividing the measurements into fixed intervals and to recognize the activity that took place within each interval.This is known as the relaxed HAR problem and an example is shown in Fig. 1 (Bulling et al. 2014).”

• cites Bulling et al. 2014 sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Datasets for HAR are typically recorded in controlled environments, where persons wear body sensors and perform a series of scripted activities.

“Datasets for HAR are typically recorded in controlled environments, where persons wear body sensors and perform a series of scripted activities (Vargas Toro 2018).”

• cites Vargas Toro 2018Not checked yet

  Not checked in the free run.

May combine two claims.

The Opportunity dataset was selected for a proof of concept because of the amount and location of wearable sensors, difference of types and complexity of target activities, relevance of target activities in a real life setting, and quantity of participants.

“This public dataset was chosen because of the amount and location of wearable sensors, difference of types and complexity of the target activities, relevance of the target activities in a real life setting, and quantity of participants in the data collection sessions (Vargas Toro 2018).”

• cites Chavarriaga et al. 2013 sourceNot checked yet

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• cites Vargas Toro 2018Not checked yet

  Not checked in the free run.

Classified as probably not a substantial claim.

To cope with intraclass variability, data from several subjects needs to be collected so that the variability of single activities can be learned by the model.

“To cope with intraclass variability, data from several subjects needs to be collected, such that the variability of single activities can be learned by the model.”

May combine two claims.

Collecting large datasets including many samples from target activities copes with the NULL class problem and interclass similarity, and the increased learning stage allows discovery of more complex patterns that enable differentiation between similar classes and NULL class activities.

“The NULL class problem and the interclass similarity are coped by collecting large datasets including many samples from the target activities.Consequently, the learning stage of the models increases allowing to discover more complex patterns that effectively enables to differentiate between similar classes and NULL class activities (Vargas Toro 2018).”

• cites Vargas Toro 2018Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Ground truth labeling is highly time-consuming and is mostly done by first recording the data collection session on video and then labeling the performed activities based on these recordings.

“Moreover, the ground truth labeling (assigning a ground truth label to each time stamp of the data stream, which is also called annotation for short) is a highly timeconsuming task.It is mostly done by first recording the data collection session on video and then labeling the performed activities based on these recordings.”

Probably not a substantial claim: may not pass S2 (checkable).

The proposed approach will increase the chance to generate data for classifiers that are compatible for patient analysis in the future.

“This approach will therefore increase the chance to generate data for classifiers, which are compatible for patient analysis in the future.”

• AssumptionRests on an assumption: Healthy people move much faster and safer than elderly or diseased people, and the type of disease also affects movements.

May combine two claims.

Measurement of ADLs allows conclusions about physical and cognitive status, provides information about frailty, and allows predictions about risk of falling.

“The measurement of ADLs allows conclusions to be drawn about the physical and cognitive status, provides information about frailty, and allows predictions about the risk of falling ( (Nourhashemi et al. 2001), (Hellström et al. 2013), (Tinetti et al. 1994)).”

• cites Nourhashemi et al. 2001Not checked yet

  Not checked in the free run.

• cites Hellström et al. 2013 sourceNot checked yet

  Not checked in the free run.

• cites Tinetti et al. 1994Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

ADLs are typically the first activities that require outside support, with hygiene being an early-loss activity, toilet-use a mid-loss activity, and eating a late-loss activity.

“These activities are typically the first ones that require outside support and it has been found that there is a progressive functional loss on them, with hygiene being an early-loss activity (i.e., this is one of the first ADL where it is likely that a person needs help from others), toilet-use a mid-loss activity, and eating a late-loss activity (Morris et al. 2013).”

• cites Morris et al. 2013 sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

Monitoring ADLs would allow estimation of independency in daily living of older adults and recommendation of activities that might improve a patient's health status.

“Monitoring ADLs would allow to estimate the independency in their daily living of older adults, as well as to recommend activities that might improve a patient's health status.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Human activities can be decomposed into four hierarchical levels: high-level activities, mid-level activities, low-level activities, and modes of locomotion.

“According to (Roggen et al. 2010), we have decomposed human activities into four hierarchical levels:”

• cites Roggen et al. 2010 sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Modes of locomotion can be used as reference for physical activity level and are useful for detecting hazardous situations such as falling.

“Additionally, modes of locomotion like walking, sitting and lying can be used as reference for the physical activity level of an individual.Furthermore, they can be useful for detecting hazardous situations such as falling (Ni et al. 2015).”

• cites Ni et al. 2015Not checked yet

  Not checked in the free run.

May combine two claims.

Micro-mobility and nutrition status are important indicators to estimate the risk profile for decubitus, especially in elder patients, and the ability to change position in bed is essential to avoid it.

“Micro-mobility (movements in bed) and nutrition status are important indicators to estimate the risk profile for decubitus, especially in elder patients.These patients constitute the single largest group (more than 60%) among all patients with decubitus ulcers (Anders et al. 2010) and their ability to change the position in bed is essential to avoid it (Harris 1996).”

• cites Anders et al. 2010 sourceNot checked yet

  Not checked in the free run.

• cites Harris 1996 sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Getting out of the bed unsupervised may lead to falls, which are a major problem in hospitals and contribute to substantial healthcare burden.

“Getting out of the bed unsupervised may lead to falls being a major problem in hospitals and contribute to substantial healthcare burden, e. g., a significant injury or an increased length of stay (( Oliver et al. 2004), (Oliver et al. 2010)).”

• cites Oliver et al. 2004 sourceNot checked yet

  Not checked in the free run.

• cites Oliver et al. 2010 sourceNot checked yet

  Not checked in the free run.

Classified as probably not a substantial claim.

For timely prevention of decubitus, it is necessary to monitor mobility and nutrition intake and detect any change indicating a risk increase.

“For timely prevention it is necessary to monitor mobility and nutrition intake and detect any change indicating a risk increase for decubitus.”

May combine two claims.

Using the toilet is clinically important because incontinence is one of the most common symptoms in neurological rehabilitation and has considerable physical, psychological and social consequences that can significantly impair quality of life.

“The activity “Using the toilet” is clinically important, because incontinence is one of the most common symptoms in neurological rehabilitation and has considerable physical, psychological and social consequences, which can significantly impair the quality of life of affected patients (Irwin et al. 2006).”

• cites Irwin et al. 2006 sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable).

It is important to monitor the drinking habits of persons with enhanced risk of dehydration.

“Therefore, it is important to monitor the drinking habits of persons with enhanced risk of dehydration (Shells and Morrell-Scott 2018).”

• cites Shells and Morrell-Scott 2018 sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Malnutrition in elderly patients undergoing rehabilitation is a prevalent and often neglected problem associated with lower rehabilitation effect and lower physical function.

“Malnutrition in elderly patients undergoing rehabilitation is a prevalent and often neglected problem associated with lower rehabilitation effect and lower physical function (Pirlich et al. 2006).”

• cites Pirlich et al. 2006 sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Elder neurological patients often show decreased thirst, which leads to dehydration and exsiccosis.

“Especially elder neurological patients often show decreased thirst, which leads to dehydration and exsiccosis (Lauster and Mertl-Rötzer 2014).”

• cites Lauster and Mertl-Rötzer 2014Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

The activPAL micro is used quite often in clinical, older aged residents and hospital trials.

“It is used quite often in clinical, older aged residents and hospital trials (Chan et al. 2017).”

• cites Chan et al. 2017Not checked yet

  Not checked in the free run.

May combine two claims.

The synchronization gesture generated clearly visible impacts on the data records of all sensors, enabling synchronization of the time axes of sensor records with camera records.

“This gestures generated clearly visible impacts on the data records of all sensors, and thus enabled to syncronise later the time axes of the sensor records with the camera records.”

May combine two claims.

Reducing the sampling frequency of SmartCardia and Myo armbands to 20 Hz is not problematic because a study found no significant gain in accuracy when using accelerometer sampling frequencies above 20 Hz.

“The reduction of the sampling frequency (and hence of the available data) for these sensors is not problematic, as Maurer et al. (2006) studied the behavior of the accuracy of models (based on accelerometer sensor data) as a function of the accelerometer sampling rate.In this study, the sampling rate was varied from 1 to 30 Hz and no significant gain in accuracy was found when using sampling frequ”

• cites Maurer et al. (2006)Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable).

It is important to recognize changes in dietary habits for timely intervention, i.e. before weight and muscle loss occur.

“In addition to monitoring the patient's weight, it is important to recognize changes in dietary habits for timely intervention, i.e. before weight and muscle loss occur.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Only one Bluetooth connection can be established on a device, so two computers are necessary for capturing the data of two Myo armbands.

“Since only one Bluetooth connection can be established on a device, two computers are necessary for capturing the data of two Myo armbands.”

Probably not a substantial claim: may not pass S3 (load-bearing).

All assistants needed for the data acquisition signed a data privacy statement.

“All assistants needed for the data acquisition also signed a data privacy statement.”

Classified as probably not a substantial claim.

The activity protocol and other study-related documents were reviewed by the Ethics Committee of the Bavarian State Medical Association, Munich.

“The activity protocol and other study related documents were reviewed by the Ethics Committee of the Bavarian State Medical Association, Munich.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

All legal requirements have been fulfilled, including insurance, qualification of study personnel, certification of sensor equipment, data protection, and a positive risk-benefit profile.

“All legal requirements have been fulfilled, i.e. the insurance, qualification of study personnel, certification of sensor equipment, data protection and a positive risk-benefit profile.”

Probably not a substantial claim: may not pass S3 (load-bearing).

At no time will there be diagnostic or medical statements or therapy adaptations based on the data acquisition trials or data analysis.

“It was also stated that at no time will there be diagnostic or medical statements or therapy adaptations based on the data acquisition trials or data analysis.”

Classified as probably not a substantial claim.

The physical and cognitive status of each participant was tested using the 5x sit-to-stand-test (5XSST), grip force test, and Montreal Cognitive Assessment (MoCA).

“In addition, the physical and cognitive status of each participant was tested.A functional test for the general fitness and frailty (5x sit-to-stand-test (5XSST), (Treacy and Hassett 2018)), the grip force test (Allen and Barnett 2011) and a test to measure the cognitive status of the participants (Montreal Cognitive Assessment (MoCA), (Nasreddine et al. 2005)) were examined.”

• cites Treacy and Hassett 2018 sourceNot checked yet

  Not checked in the free run.

• cites Allen and Barnett 2011 sourceNot checked yet

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• cites Nasreddine et al. 2005 sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Participants were explicitly informed about their rights according to the General Data Protection Regulation (GDPR).

“The participants where explicitly informed about their rights according to the General Data Protection Regulation (GDPR).”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Two technical assistants and two (medical) assistants are needed to manage the equipment and guide participants through the session according to the activity protocol.

“To optimize these procedures, two technical assistants are needed to manage the equipment as well as two (medical) assistants to prepare the participants and to guide them through the session according to the activity protocol.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

All participants signed a written consent and a data protection form, proved by the local ethical commission in accordance with the Declaration of Helsinki, and gave permission to use their video material for data annotation.

“Before participating in the study, all participants signed a written consent and a data protection form, proved by the local ethical commission in accordance with the Declaration of Helsinki and gave permission to the use of their video material for data annotation.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Elderly persons are considered vulnerable, so conceptual adjustments were made to address aspects of vulnerability.

“In general, elderly persons are considered as vulnerable and therefore conceptual adjustments had to be made to address the different aspects of vulnerability (Schroder-Butterfill and Marianti 2006).”

• cites Schroder-Butterfill and Marianti 2006 sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

The activity protocol was held flexible to allow adaptation of procedures to the abilities of the participants.

“Thus, the activity protocol was held flexible to allow the adaptation of procedures to the abilities of the participants (respecting cognitive, mental, and physical limitations).”

Probably not a substantial claim: may not pass S3 (load-bearing).

The medical assistants were actively attentive in order to adapt the activities, interrupt or terminate the participation.

“The medical assistants were actively attentive in order to adapt the activities, interrupt or terminate the participation.”

Probably not a substantial claim: may not pass S3 (load-bearing).

If necessary, participants were supported by a physical therapist throughout the entire session for safety.

“If necessary, the participants were supported by a physical therapist throughout the entire session for safety.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Ward et al. (2011) developed specialized metrics to measure the performance of a HAR classifier, defined as frame analysis and event analysis.

“Ward et al. (2011) developed specialized metrics to measure the performance of a HAR classifier, defined as frame analysis and event analysis.”

• cites Ward et al. (2011) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Statistical features (e.g., mean, variance, kurtosis and interquartile range) are popular for HAR problems due to their simplicity as well as their performance.

“A set of features is then calculated for each window: statistical features, e.g., mean, variance, kurtosis and interquartile range, are popular for HAR problems due to their simplicity as well as their performance (Bulling et al. 2014).”

• cites Bulling et al. 2014 sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

These analyses extend the traditional classification errors (false positives and false negatives) onto more descriptive categories: deletion, insertion, merge, and fragmentation.

“These analyses extend the traditional classification errors (false positives and false negatives) onto more descriptive categories: deletion, insertion, merge, and fragmentation.”

• cites Ward et al. (2011) sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Frame analysis corresponds to error categories calculated with regard to the single frames (time windows) of the classifier.

“Frame analysis corresponds to error categories that can be calculated with regard to the single frames (time windows) of the classifier.”

• cites Ward et al. (2011) sourceNot checked yet

  Not checked in the free run.

Classified as probably not a substantial claim.

An event is equivalent to a sequence of windows that represent the same activity.

“In contrast, an event is equivalent to a sequence of windows that represent the same activity.”

• cites Ward et al. (2011) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

The authors of the Opportunity dataset released a public challenge to recognize hand gestures and locomotion activities.

“The authors of the dataset released a public challenge to recognize hand gestures and locomotion activities.”

• cites Chavarriaga et al. 2013 sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based).

Critical situations (e.g., fall, immobility or dehydration) could then be recognised both for patients in the hospital and for elderly people at home, such that care staff could intervene earlier.

“Critical situations (e.g., fall, immobility or dehydration) could then be recognised both for patients in the hospital and for elderly people at home, such that care staff could intervene earlier.”

• AssumptionRests on an assumption: The HAR system can reliably recognize critical situations such as falls, immobility, or dehydration.

Classified as probably not a substantial claim.

The analysis of activity pattern and possible pattern deviations allows risk assessments without the affected person being permanently observed and without intrusion into the persons' privacy.

“The analysis of activity pattern and possible pattern deviations allows therefore risk assessments without the affected person being permanently observed and without intrusion into the persons' privacy.”

• AssumptionRests on an assumption: Using unobtrusive sensor-systems does not intrude into the persons' privacy.

Classified as probably not a substantial claim. May combine two claims.

The TA-EG is a 19-item questionnaire consisting of four dimensions: enthusiasm for technology, competence in dealing with technology, positive and negative attitudes towards technology.

“The TA-EG is a 19-item questionnaire consisting of four dimensions, i.e., enthusiasm for technology, competence in dealing with technology, positive and negative attitudes towards technology.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The TA-EG is one of a few scales that is not restricted to computer use.

“Therefore, the Affinity for Technology questionnaire (TA-EG) of electrical devices was used, which is one of a few scales that is not restricted to computer use ( (Karrer et al. 2009), (Attig et al. 2017)).”

• cites Karrer et al. 2009Not checked yet

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• cites Attig et al. 2017Not checked yet

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Probably not a substantial claim: may not pass S4 (based). May combine two claims.

By using unobtrusive sensor-systems in the respective room, the assistance of professional caregivers could be reduced and therefore lead to a more private environment.

“By using unobtrusive sensor-systems in the respective room, the assistance of professional caregivers could be reduced and therefore lead to a more private environment.”

• AssumptionRests on an assumption: The proposed HAR system can enhance quality of life, privacy, and autonomy and reduce carer effort.

Probably not a substantial claim: may not pass S3 (load-bearing). The quote only partly states this claim.

The responses to all TA-EG items are categorized on a 5-point Likert scale, ranging from 'fully agree' to 'fully disagree'.

“The responses to all items are categorized on a 5-point Likert scale, ranging from”