1. Introduction
In Singapore, suicide continues to be the leading cause of death for people between the ages
of 10 and 29. In 2021, sucide rates for this age group has risen by 23.3% from the past year
(Samaritans of Singapore, n.d). According to data gathered over the past few years, suicide
mortality among youths has been an increasing societal concern. The main factor causing this
is depression. A distressing life event, such as a divorce, job retrenchment and the loss of a
loved one, can lead to depression, a mental health disorder. Some of us may have gone
through this tragic experience at least once in our lives, and if one lacks the mental support to
deal with their grief, it may lead to self-harm or worst yet, suicide. This marginalized group
of people suffering from depression are either too ashamed to recognise the fact that they
require help or they are showing slow hidden signs of depression unknown to them. Thus, to
prevent suicide, there must be an emphasis place on the early detection of depression. Early
detection would also mean a person can undergo recovery treatment. To achieve that, this
research paper would discuss how AI alongside with wearable technology, help to diganose
the various “warning signs” of high stress levels such as physical interaction and speech
recognition.
2. Context & Justification
To further contextualize the issue, relying on emergency and crisis hotlines for rapid
intervention has proven to bring comfort to somebody in distress in the moment but there is
no scientific evidence that it will be proven effective in the long run (Kazdin, 2018).
Additionally, there has been reports showing that Singaporeans hide their depression rather
than seek appropriate treatment such as counselling. This is hugely attributed to the fear of
judgement from the society as there is a social stigma against mental health (Adelphi Psych
Medicine Clinic, n.d). From these 2 main problems identified, we seek to establish an AI
solution with the incumbent wearable technology- Apple Watch, for curing depression in the
long run and self-diagnosis of current condition which helps in suicide prevention.
In 2021, more than 100 million people wears an Apple Watch (Cybart, 2022) for not only its
aesthetic but also its software features that revolves around health monitoring. A survey
conducted on 1500 Apple watch owners has shown that 83% of which have said the device
only somewhat contributes to their overall health and fitness (Pai, 2015). Some key
functionalities are tracking the quality of sleep, heart-rate monitoring and medications
reminder (Apple, n.d.). Apart from monitoring your heart rate from the screen of the Apple
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Watch and being alerted on irregular heartbeats, the data collected is not able to properly
translate into some form of descriptive diagnosis on our actual health. Since the adoption of
the Apple Watch have been increasing over the years, I feel that if we could incorporate a
more thorough integration of AI into the wearable, it could be very powerful and
advantageous to the society in the years to come.
3. Recommendations
To maximise the AI capabilities of the wearable technology, I will be incorporating the
suggestions gathered from other literature reviews covering on the topics of public health and
wearable technology.
1. To ensure the wearable has a round-the clock charging technology through employing
a WIFI based energy harvesting so the data collected will be consistent and accurate
as users are not required to remove the device for charging. While staying connected
to the WIFI for power, another functionality that can be incorporated is StressMon - a
stress detecting system which will evaluate a student’s behavior.
2. Apart from monitoring heart-rates, there can be other methods of detection ie. voice
recognition through machine learning / neural networks. From the data collected,
wellness/ counselling centers located in the student’s university will be alerted to
arrange an appointment with these students to explore and understand what
difficulties the students are facing.
In short, our framework to help detect and treat the early signs of depression are:
1. Prognostics over diagnostics : Early detection of symptoms
2. Monitoring the data collected: Data translated to reports for the users, sent on a
weekly/ monthly basis.
3. Communicating the relevant health data stored to the counselling and wellness centers
for counselling treatment
4. Incorporating the huamn element: Diagnosis by mental health specialist when the
patient is experiencing forementioned symptoms for a prolonged period of time
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4. Technical Analysis
With the recommendations listed earlier, we will cover the technical implementation in this
section.
Machine learning (Classification Model, Supervised Learning)
StressMon (Zakaria et al., 2019, #)
This is a stress and depression detection system that leverages single-attribute location data
through the use of the WIFI infrastructure. It extracts a detailed set of physical group
interaction pattern features without any user actions or software installations on phones which
makes this solution more ideal to other systems which requires an application. These features
are then passed through two different machine learning models which help to detect stress
and depression. While most sensory applications or technology focuses on individual health
factors such as heart rate, this method focuses on physical interaction, a critical element in a
student’s life. The stress detection machine learning model is trained based on various
sources of data collected on students , ie. Big 5 Personality test, campus routines and mental
states. Based on the results collected from the change in group interaction pattern , the data
will be mapped to each feature vector as labels and the output of the classifier will be
classified under 4 categories :Severe stress, Normal Stress, Depressed and Non-depressed.
Application: This would be a great addition to the Apple Watch functionality since the Apple
Watch have existing WIFI capabilities to implement this system. Once the machine learning
model has derived with a predicted outcome regarding stress levels, our peers who is in
closest proximity to the person experiencing high stress levels, are able to help “counsel” and
alleviate the person experiencing stress. This not only makes help readily available around
us, it fosters a greater sense of the SMU culture among students. To encourage students to
take the initiative, these students can be recognised through SMU newsletter to reward them
for their exemplary behavior.
Deep Learning -Neural Network (Wang et al., 2021, #)
Instead of drawing conclusions from data collected based on our senses like heart-rate and
sleep quality which may contain some “noise” while trying to interpret the data, we can look
at speech recognition with the use of deep learning -neural networks. A speech emotion
recognition system plays an important role in judging the change of mental state and emotion
(Wang et al., 2020). The voices of a person with high stress levels tend to be labored with
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slight slurring of speech (Smith, 2017). In order to incorporate this system, first we have to
extract speech features associated with a depressed and non-depressed student. To do that, we
need to use a technique called sequence modelling which feeds the model sequences of text
and audio data from questions and answers collected from both individuals (Matheson, 2018).
Words associated with depression includes “sad” , “low” or “down”. The labels of a speech
signal can either be a classification or a regression problem depending on how the speech is
labelled. The key lies in successfully mapping speech signals to depression features in order
to successfully determine if the individual is displaying any signs that were predictive of
depression.
The process will look like this :
Step 1: Collect speech data sets
Step 2: Speech Data cleaning and sorting
Step 3: Extract speech features
Step 4: Deep learning methods are used for model construction and classification
Step 5: Test and Output
Application: Since Apple stores transcripts of our interactions with Siri in order to review
them and to improve the system (Apple, 2022), we are able to implement the voice detection
system where we incorporate deep learning to analyse the data and to detect stress levels
based on a person’s daily communication using a speech recognition software. To develop
this system further, we could create an application where parents and wellness centers will be
notified through an API. This will allow immediate steps to be taken to reduce a person’s
stress level.
5. Social Analysis
Benefits
Stress is inevitably a normal part of our lives and how we manage is entirely up to us. With
the AI tools at our fingertips, we are able to identify students who are struggling with school
work or experiencing an inner battle with mental illness such as depression. While not
entirely removing the human element of diagnosing the cause of depression, the system is
helping us to identify early symptoms. Additionally, it has been seen that many hospitals like
NUHS is also compliant with the Personal Data Protection Act (PDPA) which serves as a
foundation in allowing continuous innovation without users worrying about data being stolen.
As more healthcare providers in Singapore enforces the importance of PDPA to prevent a
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data breach, we are likely to see a more seamless integration of AI in our everyday lives.
Lastly, only 47.3% of mental health cases are detected accurately by professionals (Kesari,
2022). By incorporating AI into detection of mental health, we are able to monitor closely
and yield more accurate results.
Risks
One of the main drawback for the StressMon system is that it uses location data and evaluates
temporal changes in an individual’s routine. To retrieve the data, one is required to be
connected to the WIFI and campus buildings are indoors so GPS is unavailable. This solution
makes it tough to scale because one has to rely on collecting location traces in different parts
of the building and it might be time consuming to do that. Additionally, this system does not
consider the externalities that the person is facing, for example, the stress can be derived from
dealing with problems at home or an unfortunate event that recently occurred. Just by basing
on the criteria that the person is acting out of the norm is not sufficient to justify that he or
she is experiencing high stress levels.
For the second proposed solution, since the user is required to install an application on their
device, makes this resource hard to scale to large groups of user populations and it introduces
a strong self-bias where only users who are interested in getting help would receive it.
Further, these apps pose high privacy risk as they collect and analyse a rich set of personal
data (e.g.fine-grained location, conversation). Furthermore, it will not be easy to get high
volumes of good-quality data.
Another concern is the research on mental health and wearables are still at the infancy stage
and more data is required to improve the results of the predictive outcomes. People who owns
an Apple Watch tends to be more technologically-savy, and if these two solutions requires
advanced technology where the elderly in the society does not possess, it would make it hard
to scale when we move to another set of demographics in the future. Lastly, my research has
shown that there are many other wearable devices which tracks various vital signs, for
instance, brain waves. We need to acknowledge the fact that the watch has its limitations
where it can provide surface level tracking but not a “one solution fits all” method to track
mental health.
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6. Conclusion
Based on our solutions identified above, there are still other features that have not been
discussed. Some other features that can be explored is the gesture identification technology to
identify a display of “problem behavior”, scanning through one’s text messages to identify
words that implies stress through machine learning and tracking of sleep quality with the
wearable without being worn through pressure sensors and radio frequency with the use of a
classification model.
In conclusion, while AI is proven beneficial and widely used to detect stress, there is still the
incorporation of the human element at the end. We are not using AI to replace humans but
instead it augments human intelligence to provide continuous monitoring of one’s health with
precision.
Word Count: 2013 words
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