The
progressive ageing of the global population is leading to an increasing
number of people affected by cognitive decline and dementia [1].
Particularly, it is expected that the number of people suffering from
Alzheimer’s Disease (AD) (accounting for 60–65% of the dementia cases)
will reach 74.7 million by 2030 and 100 million by 2050 [2].
Even though dementia is mainly associated to the prototypic memory
loss, different cognitive domains can be affected by different
pathologies, leading to distinct cognitive symptoms. Among them, the
executive functions (EFs) represent a complex construct that involves
cognitive, behavioural, and emotional aspects. Deficits in EFs can be
defined as “dysexecutive syndrome” [3],
which includes cognitive (e.g., deficits in response inhibition, rules
deduction, set-shifting, information generation, action planning,
response initiation, coordination of dual-tasks) and/or behavioural
(e.g., hypoactivity, apathy, distractibility, preservative behaviour,
social behaviour) alterations [4].
Currently, EFs are clinically evaluated mainly administering standardized neuropsychological tests [5]
such as the Frontal Assessment Battery (FAB) and the Behavioral
Assessment of Dysexecutive Syndrome (that evaluate the EFs as a whole),
and tests assessing specific aspects of EFs, such as the Trail Making
Test (TMT, for divided attention and working memory), the Stroop
Interference Test (for response inhibition), the Digit Span (for verbal
working memory), or the Tower of London (for planning).. Although
neuropsychological testing is today the gold standard to assess
dysexecutive symptoms, a recent literature review highlighted that they
present several limitations. The validity and reliability of the test
results are sometimes limited because of normative data based on small
datasets, some of the cognitive domains are scarcely represented, while
others are assessed in different tasks, many tests are available in a
restricted number of languages, and sometimes cultural habits can affect
the execution of the required tasks [6].
In
this context, new protocols and novel tools to assess
neuropsychological functions should be investigated. In this work, we
focus on the decline of EFs in motor programming that results in action
planning impairments.
In the past years, some experimental studies
have proposed reach-to-grasp (RG) protocols to highlight how the
intentions can influence the action planning. RG sequences analyses have
revealed that healthy subjects differently reach and grasp an object
depending on the action final goal [7] because people are driven by prior intentions. Kinematics conveys information about these intentions [8], so that, even if the object to-be-grasped is the same, different motor parameters can be appreciated [9].
Therefore, when someone reaches and grasp a bottle to pour its content
into a container or, conversely, to pass it to someone else, modulation
of the kinematic action occurs. Furthermore, previous studies that
implemented experimental protocols based on RG and after-grasp (AG)
sequences have revealed useful information in several pathologies, such
as Parkinson’s Disease [9, 10], autism spectrum disorder [11, 12], and stroke [13].
Reach-to-grasp
tests are easy to be performed and can overcome languages and cultural
bias. Nevertheless, up to now, the traditional methodologies employed to
analyze motor performance during such tasks are mainly based on motion
optical capture systems, which are expensive, require lengthy procedures
and dedicated staff for set-up and analysis, and are applicable in
dedicated wide settings only.
Recently, advances in Micro
Electro-Mechanical Systems, and in artificial intelligence (AI)
techniques, have allowed employing wearable technology, together with
processing and learning algorithms, to evaluate experimental protocols,
both in lab and in clinical settings. This represents a promising
solution for objective and reliable monitoring, assessment, and support [14].
Hence, wearable inertial devices have been used, so far, to acquire and
process high-frequency rate data to analyze motion performances within
several applications, including daily activity living gestures [15], early Alzheimer’s detection [16], Parkinson’s disease assessment [17], mild cognitive impairment evaluation [18], and autism spectrum disorder assistance [19].
In
this context, we propose SensRing: a non-invasive, low-cost,
lightweight, easy-to-use, ergonomic device able to capture the 3D
movements of a finger in the space. In a previous work, we evaluated the
accuracy of this device in measuring kinematic parameters in healthy
people during standard exercises [20].
Here, the device is proposed within a pilot study, for the use in a
clinical application with people suffering from mild cognitive decline
and healthy controls. The SensRing allows the measurement of the
kinematic parameters related to the motor performance without
interfering on motion capabilities.
This study proposes the use of
SensRing as an alternative approach to traditional methods aiming to
objectively analyze RG and AG sequences. Since Mild Neurocognitive
Disorder (MND) subjects, with a decline in EFs, often show impairment in
motor programming and action planning [21],
the cognitive decline could be objectively confirmed through kinematic
parameters’ variations. The hypothesis is that the cognitive decline
could be identified objectively examining the kinematic parameters. The
idea is to investigate: (i) whether the kinematic performance of people
diagnosed as MND with EF impairment is different compared to older
healthy controls, during a simple motor protocol, and (ii) whether there
are differences in action kinematic modulation depending on the action
end-goal between MND and healthy subjects. Finally, we evaluated whether
the kinematic parameters could be correlated to the traditional
cognitive assessment based on clinical scores.
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